Artificial Intelligence applications in due diligence processes for large-scale merger and acquisition transaction evaluation

 Co esponding au ho : Angela Chidindu Ugoji.
Copy igh © 2025 Au ho (s) e ain he copy igh o his a icle. This a icle is published unde he e ms o he C ea i e Commons A ibu ion Liscense 4.0.
A i icial In elligence applica ions in due diligence p ocesses o la ge-scale me ge
and acquisi ion ansac ion e alua ion
Angela Chidindu Ugoji *, Iyedolapo Ajewole, Olaadu a Abigail Pe e s and Culbe Kalle
Depa men o Business Adminis a ion, Wea he head School o Managemen , Case Wes e n Rese e Uni e si y, Ohio,
Uni ed S a es o Ame ica.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
Publica ion his o y: Recei ed on 18 July 2025; e ised on 24 Augus 2025; accep ed on 26 Augus 2025
A icle DOI: h ps://doi.o g/10.30574/wja .2025.27.2.3057
Abs ac
Backg ound: A i icial in elligence in eg a ion in due diligence p ocedu es o mega-la ge me ge acquisi ion dealings
has been b ough ou as a powe ul me hod in he Ame ican business wo ld. Con en ional due diligence p ocesses end
o ha e a weakness when handling la ge olumes o inancial and ope a ional in o ma ion in an e icien manne .
Ad anced analy ics and machine lea ning algo i hms ha e p o en o hold a lo o p omise in e ms o imp o ing he
accu acy o he e alua ion o ansac ions, and also in e ms o mi iga ing ime p essu es ha a e no mally p omulga ed
in iew o in ica e MandA assessmen s. The de elopmen o AI-based ools has p o ided ways whe e inancial
ins i u ions and co po a e o ganiza ions can make be e s a egic decisions by analysing all he da a and p edic ing he
ou comes. The AI echnologies p o ide imp o ed ac i i ies in isk assessmen , iden i ica ion o a ge s, and e alua ion
o ansac ions in a ious ma ke s in he USA. The machine lea ning algo i hms will make i possible o e alua e he
inancial esul s, ope a ional syne gies, and ma ke posi ioning aspec s ha de e mine success a es o ansac ions
wi h g ea e p ecision.
Ma e ials and Me hods: The esea ch used a sophis ica ed and igo ous me hodology o seconda y da a analysis as i
analyzed 215,160 c oss-bo de M and A deals and conce ned da a in he Thomson Reu e s SDC Pla inum da abase o e
he 1973-2018 pe iod. We ha e applied he machine lea ning algo i hms based on AdaBoos and suppo ec o
machine models as a p edic i e analysis. The p ep ocessing o da a in ol ed ea u e ex ac ion o ESG sco es, inancial
indica o s and he me ics o sus ainable de elopmen a coun y-le el. The s udy in ol ed a o al o 215,160 c oss-
bo de M and A deals in 58 s a es wi h special a en ion paid o hose based in he USA. The da a p ep ocessing in ol ed
ea u e ex ac ion, p incipal componen analysis and 10- old c oss- alida ion me hods.
Resul s: Analysis indica ed ha AI-based due diligence pla o ms ha e a p edic ion accu acy o 80.1% in he
de e mina ion o me ge success when compa ed o adi ional app oaches, which had 62.7% accu acy. ML algo i hms
a e good a wo king wi h mul i-dimensional da ase s such as he ones in ol ing ESG cha ac e is ics, inancial indica o s,
and ma ke in elligence ac o s. The applica ion o na u al language p ocessing echnologies cu s down ime ames o
con ac analysis by 60-70% wi h no loss o egula o y compliance assessmen . The amewo ks o isk e alua ion
in eg a ing AI algo i hms ou pe o m in iden i ying possible ansac ion hu dles and syne gy in he a ious sec o s o
he indus y.
Discussion: The e ec i eness o AI applica ions in due diligence e iciency and accu acy has been seen in signi ican
ma ke s o he USA. Machine lea ning algo i hms a e e ec i e a p ocessing mul i-dimensional da a such as
en i onmen al, social and go e nance ac o s and hey minimize human bias in a ing ansac ions. The echnology can
eal- ime isk e alua ion and an imp o emen in decision making abili ies o USA co po a ions unde a ious
geog aphical ma ke s. The use o amewo ks based on AI acili a es s a egic decision making by elimina ing human
e o and bias in making he complex ansac ion decisions.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
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Conclusion: Machine lea ning p og ams can g ea ly imp o e he e iciency and p ecision o he due diligence p ocess
in la ge-scale me ge and acquisi ion businesses by making i easie o check he analysis wi h g ea e de ail and speed.
Fu u e de elopmen o AI echnologies is likely o enhance u he p edic i e modeling, isk e alua ion and he p ocess
o s a egic analysis in he in e na ional asse acquisi ions and sales ma ke place.
Keywo ds: A i icial In elligence, Me ge and Acquisi ion, Machine Lea ning, P edic i e Analy ics, Na u al Language
P ocessing, S a egic E alua ion.
1. In oduc ion
Me ge and acquisi ion ma ke as a whole and pa icula ly in he Uni ed S a es o Ame ica (USA) is one o he mos
sophis ica ed and capi al-in ensi e sphe es o co po a e inancing whose ansac ions olume su passes 3.5 illion
dolla s in he yea ly a e and co e s he b oad business en i onmen s (Johnson e al., 2022). A i icial in elligence
echnologies used in due diligence p ocesses ha e changed he ace o me ge and acquisi ion ac i i ies in he Uni ed
S a es in a undamen al way by a oiding pi alls associa ed wi h human ac o s. The esea ch conduc ed by Rahman
(2021) on he ole o AI in MandA ansac ions shows ha he use o con en ional due diligence p ocesses is being
supplemen ed and e en eplaced by he ad anced machine lea ning algo i hms ha can analyze massi e lows o bo h
inancial and ope a ional da a wi h unma ched p ecision. Based on he indings o a s udy conduc ed by Kajewole e al.
(2023) on composi e AI and blockchain implemen a ion in he p ocess o MandA, he concep o in elligen sys ems has
ans o med he manne in which co po a ions engage in he ansac ion e alua ion and isk assessmen p ocedu es.
In addi ion, Baumga ne (2024) poin s ou in hei esea ch on he in luence o AI in due diligence ha inancial
ins i u ions in con empo a y socie y would need ad anced di e ences o main ain ele ance in he cons an ly dynamic
ma ke place si ua ion.
A i icial in elligence can ans o m he pe spec i e o MandA due diligence beyond i s exis ing imp o emen s in e ms
o e iciency o he co e o decision-making capabili ies and isk managemen app oaches. The s udy published by Li
(2018) on he use o AI echnology in he ield o me ge s and acquisi ion shows ha machine lea ning algo i hms can
disco e he pa e ns and ela ionships in inancial in o ma ion ha could no ha e been ealized o misin e p e ed
wi h he old analy ics echniques. In he same line, esea ch by Ma qua d e al. (2023) on isks and oppo uni ies o AI
associa ed wi h MandA p ocesses e ealed ha in elligen sys ems o e highe accu acy o alua ion o a ge
companies and, a he same ime, dec ease he ime needed o comple e ho ough e alua ion o ansac ions. Fu he ,
Wya e al. (2022) ha e managed o esea ch he in eg a ion o AI in o he MandA p ocesses by showing ha companies
adop ing new echnologies will exhibi a as ly inc eased success a e o he deals in ques ion along wi h pos -me ge
in eg a ion.
Figu e 1 AI Due Diligence
The his o ical de elopmen o he due diligence p ocesses in execu ing la ge-scaled MandA ansac ions is he ca alys
o he bigge his o ical end in echnological de elopmen o he Ame ican inancial se ices indus y. A piece o
esea ch by Honcha enko (2024) on gene a i e AI ans o ma ion o due diligence p ocesses in MandA explained ha
he inco po a ion o na u al language p ocessing and machine lea ning inno a ions acili a ed he sc u iny o in ica e
counsel ag eemen s and egula o y disclosu es by inancial ins i u ions as e and wi h un i aled sc u iny. Nogh ehka
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
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(2023) in hei esea ch on he cu en and u u e ole o AI in MandA, s a e ha he applica ion o p edic i e analy ics
enables in es men banks and co po a e de elopmen eams o be e posi ion hemsel es s a egically by using all he
da a a ailable o base hei judgmen s and decisions on ins ead o being cons ained by ime-wo n inancial a ios and
subjec i e e alua ions.
Use o a i icial in elligence in due diligence p ocedu es is one o he mos impo an inno a ions o be in oduced o
he co po a ions in Ame ica so ha hey can con inue enjoying compe i i e ad an ages in he global ma ke s. Acco ding
o esea ch by Bhagwan (2020) on AI uses in a ge selec ion and due diligence conside a ions, he machine lea ning
algo i hms ha e a pa icula ad an age in he iden i ica ion o po en ial syne gies and isks o in eg a ion ha human
analys s may no see in cus oma y e iew p ocess. This was demons a ed in a s udy by Ibo (2025) which showed ha
AI especially comes in handy in c oss-bo de deals whe e cul u al, egula o y, and ope a ional issues would necessi a e
he use o ad anced analy ical de ices o he han he s anda d assessmen measu es.
Recen due diligence egula ions necessi a e he implemen a ion o ex ensi e assessmen models ha could p ocess a
a ied ange o in o ma ion sou ces and ye no lose i s p ecision and egula ion le els. Resea ch by Abbasli (2024) on
how o imp o e upon due diligence whils main aining compliance sugges ha AI in eg a ion needs o be e he ed in
all co ne s o egula ion and e hical issues o achie e posi i e ea u es o sus ainable applica ion in di e en indus y
sec o s. Based on a s udy by Adewunmi (2016) on acqui ed ways o o e coming he challenge in he ield o MandA wi h
he help o AI ools, he p ospec s o success ul echnology in oduc ion imply long- ange planning and he abili y o
s udy o ganiza ional po en ial body as well as he ma ke en i onmen . As Li e al. (2022) s a e, based on hei esea ch
o he impac o AI in he due diligence o c oss-bo de MandA, echnological solu ions should be ailo ed o pa icula
local and sec o -speci ic needs o be implemen ed e icien ly and wi hou isks o ailu e.
T ansac ions in c oss-bo de me ge and acquisi ion equi e ad anced analy ical ools ha can handle a wide a ie y
o da a sou ces, and a ia ions in he egula ions. Adewunmi (2016) says ha solu ions o such challenges as
in o ma ion asymme y and ansac ion complexi y can be ound in a i icial in elligence when doing ex ensi e esea ch
on he ways o na iga e he challenges o a me ge in Nige ia. Along wi h con en ional app oaches o due diligence, Li
e al. (2022) also s a e in hei wo k ha a i icial in elligence a ec s he p ocedu e o conduc ing due diligence be ween
coun ies due o he inc eased in es iga ion powe . On he same no e, a de ailed s udy conduc ed by Liu (2000) has
s ipula ed ha combina ion o a i icial in elligence and me ge ac i i ies has aken a mode n-day change in he deligh
o co po a e ansac ions assessmen . In addi ion, Gup a (2022) in his s udy compa ing human wi h machines has also
highligh ed he e ec s o a i icial in elligence on legal due diligence in me ges o co po a ions.
Highligh s
• AI echnologies a e e olu ionizing due diligence p ocesses in M&A ansac ions ac oss USA coun ies and ci ies
• Machine lea ning models achie e 80.1% accu acy in p edic ing me ge and acquisi ion success a es
• ESG ac o s and sus ainable de elopmen cha ac e is ics signi ican ly in luence AI-d i en M&A decision-making
• AdaBoos and SVM classi ica ion models demons a e supe io pe o mance in c oss-bo de ansac ion e alua ion
• Financial isk ac o s including size, p o i abili y, and alua ion emain c i ical p edic o s in AI-enhanced due
diligence
• Au oma ed con ac analysis and documen e iew educe due diligence imelines by up o 60% in USA ma ke s
1.1. E olu ion and His o ical De elopmen o A i icial In elligence in Me ge and Acquisi ion Due Diligence
1.1.1. His o ical Founda ion o Due Diligence P ocesses in Me ge and Acquisi ion T ansac ions
The concep o due diligence p ocesses when ca ying ou co po a e dealings, pa icula ly in Ame ican co po a ions,
has passed h ough a ious s ages and his o ical lines ha help in unde s anding how he in eg a ion o a i icial
in elligence in he p ocess o MandA ac i i ies has e olu ionized he whole p ocess. In s udy by Chen e al. (2023) abou
using AI echnology in me ge s and acquisi ions, in he i s hal o he 20 h cen u y, adi ional app oaches o due
diligence came in o o ce when co po a ions s a ed being in ol ed in mo e challenging and complex cases o business
combina ions and needed a ho ough e alua ion o any a ge company asse s, liabili ies, and ope a ions po en ials.
Acco ding o esea ch conduc ed by Bedeka e al. (2024) on he e iciency o he AI in due diligence p ocesses, ini ial
due diligence p ac ices equi ed cumbe some and subjec i e p ocesses o manually e iewing documen s and based on
manual judgmen , which ook a lo o ime and p o ed inaccu a e in complica ed ansac ional si ua ions.
The o mula ion o s anda dized due diligence sys ems in he mid-1980s was an indica ion o expanding egula o y
p essu e and he complexi y o ansac ions in he Ame ican inancial ma ke s. A s udy by ACS Moschne and Co. (2019)
on he use o a i icial in elligence in he p ocess o ca ying ou M and A due diligence p o es ha con en ional
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
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app oaches ocused on e iewing documen s as ex ensi ely as possible, analyzing inancial condi ions o a ge s, and
assessing ope a ions o disco e po en ial isks and oppo uni ies o a a ge company. As Mangaldas (2020) has ound
on he e ec i eness o AI in due diligence p ocesses, such adi ional me hods ypically needed la ge human esou ces
and conside able in es men o ime ha made hei pe o mance no as e ec i e as needed in as -changing ma ke
dynamics. As hei s udy on a i icial in elligence applica ions e eals, echnological ad ancemen was slow and i was
mainly dealing wi h he enhancemen o he da a s o age and e ie al p ocesses.
Compu e iza ion o da a p ocessing sys ems ha ook place in la e 1990s ma ked he s a o echnological in eg a ion
in o due diligence p ocesses. Acco ding o he s udies by Ahmed e al. (2024) ega ding he applica ion o AI o a ge
iden i ica ion and due diligence app oaches, he ini ial au oma ion cases we e cen ed a ound he inancial da a analysis
and documen managemen sys ems in ended o enhance e iciency wi hou changing he es ablished analy ical
pa adigm. Acco ding o he esea ch by Bizjou nals (2024), such ea ly echnological e o s ga e a ma ginal inc ease in
speed o p ocessing bu did no sol e he co e p oblems o lack o analy ical capabili ies and eliabili y o p edic i e
models. Along wi h digi al abili ies, i has also ollowed ha echnological imp o emen has mean ha elec onic
echnologies b ough abou anspa ency o ansac ions and minimized in o ma ion asymme ies which ampli ied on
accoun o hei esea ch on MandA (Wu e al., 2018).
1.2. Fundamen al Concep s and Te minology in A i icial In elligence Applica ions o MandA Due Diligence
1.2.1. De ini ion o A i icial In elligence in MandA Con ex
A i icial in elligence in me ge and acquisi ion si ua ions e e s o he ad anced compu ing sys ems ha a e used o
imi a e human men al unc ions in he p ocess o e alua ing ansac ions. Resea ch by Nogh ehka (2023) on how
MandA p ocesses can be ans o med wi h he help o AI p o es ha his ype o sys em employs he use o machine
lea ning algo i hms o compa e and ma ch complex pa e ns in inancial da a. Bhagwan (2020) in hei s udy on he
subjec o AI and a ge iden i ica ion disco e ed ha AI has he capabili y o consume la ge olumes o s uc u ed and
uns uc u ed da a simul aneously. As pe he esea ch conduc ed by Ibo (2025) on MandA challenges na iga ed wi h
he help o AI, he echnology makes i possible o analyze a ious pa e ns au oma ically and make p edic ions. Also,
acco ding o a s udy by Abbasli (2024) on he opic o due diligence and compliance i is possible o s a e ha AI
algo i hms can e ain he le el o compliance and e en i can be inc eased, a he same ime accele a ing he
e ec i eness o he analy ical p ocesses.
The mechanism o AI ope a ional in MandA unc ions is ha o mul iple and in e wined pa s ha ope a e in a
syne gis ic manne o imp o e he decision-making s eps. In es iga ions on AI use in o e coming challenges o MandA
in Nige ia conduc ed by Adewunmi (2016) show ha ML algo i hms could ecognize obscu e ela ionships be ween
inancial quan i ies and ansac ion esul s. Li e al. (2022) conduc ed an ex ensi e su ey ha e ealed ha neu al
ne wo ks ha e he abili y o p ocess mul idimensional da a o c ea e he insigh ha could aid in he p edic ion. As Liu
(2000) a gues based on s udies owa ds in eg a ing a i icial in elligence in o con empo a y MandA, hese sys ems ha e
he abili y o conside bo h inancial, ope a ional and s a egic en i ies a he same ime. As he s udy conduc ed by
Gup a (2022) o examine how AI will a ec legal due diligence indica es, au oma ed sys ems a e capable o ensu ing
uni o mi y o analy ical s anda ds among a ious ypes o ansac ions.
1.2.2. Machine Lea ning Applica ions in T ansac ion E alua ion
T ansac ion analysis wi h he use o machine lea ning echnologies is he b eak h ough in he ansac ions analysis
which means shi ing he old- ashioned me hods o analysis o mo e ad anced algo i hmic echniques. As a s udy by
Chen e al. (2023) on he AI echnology applied o MandA shows, using supe ised lea ning algo i hms, i is possible o
lea n he success a es o ansac ions based on he pa e ns in he pas . The wo k by Bedeka e al. (2024) on he opic
o AI e ec i eness in due diligence p o es ha he unsupe ised lea ning me hod is able o ind links be ween he
a iables ha we e p e iously e oneously o e looked. Responding o he ques ion o whe he deep lea ning models
can p ocess complex inancial s a emen s wi h p ecision, ACS Moschne and Co. (2019) said ha he answe o his
ques ion is in he a i ma i e. Also, as he a icle by Mangaldas (2020) on he e ec i eness o AI in due diligence shows,
ensemble me hods may use se e al di e en app oaches o algo i hmic p ocedu es o p oduce be e p edic ion
pe o mance.
1.2.3. De ini ion and Concep ual F amewo k o Due Diligence in Co po a e T ansac ions
Au oma ion o due diligence p ocess ia a i icial in elligence is a c ucial way o change he o ganiza ion o assessing
he po en ial me ge and acquisi ion oppo uni ies. Resea ch by Wu e al. (2018) p oposing AI-enhanced due diligence
shows ha due diligence should aim a au oma ion ha will use au oma ed sys ems o p ocess legal documen s,
inancial s a emen s, and ope a ing in o ma ion p ocesses concu en ly. As pe s udies conduc ed by Siew e al. (2022)
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on he u u e o MandA wi h he help o AI guidance, au oma ized analysis o a con ac will allow de ining possible isks
and oppo uni ies in a eco d ime. As Choi a al. (2023) conduc ed an inqui y on he combina ion o AI wi h MandA, he
esea che s e ealed ha na u al language p ocessing may be used o ex ac such c ucial da a in he uns uc u ed
o ms. Also, a s udy conduc ed by Xu e al. (2023) on he ole o AI in Chinese MandA due diligence shows ha
compu e ized sys ems will be able o ensu e consis ency in c oss-cul u al and egula o y se ings.
Technical a chi ec u e o he au oma ed due diligence sys ems is connec ed wi h a se ies o he analy ical p ocessing
le els and mechanisms o con olling quali y. As Rien (2018) says, in he s udies o he impac o AI on MandA s a egy,
hese sys ems con ain da a alida ion algo i hms o make su e he in o ma ion can be conside ed alid. A s udy by Bake
e al. (2024) on he AI-powe ed deals p o es ha complex documen hie a chy and in e dependencies a e unde con ol
when au oma ed wo k lows a e used. Johnson e al. (2022) in i s pape on he p ac ices o machine lea ning in c oss-
bo de MandA lea ned ha he au oma ed sys ems a e capable o coping wi h a ious egula o y demands in
ju isdic ion. Acco ding o s udies by Nguyen e al. (2023) on g aph-based deep lea ning and MandA p edic ion, i u ns
ou ha ne wo k analysis is capable o decoding la en connec ions among en i ies.
1.2.4. La ge-Scale T ansac ion E alua ion Me hodologies and Analy ical F amewo ks
The la ge-scale me ge and acquisi ion ansac ions in ol e a e y complica ed assessmen echnique ha can be able
o analyze he complica ed inancial, ope a ional and s a egic da a ac oss se e al business en i ies and geog aphical
loca ions. In he s udy by Pe o-Ko honen El Bouch ili (2020) o AI-d i en alua ion me hods in la ge-scale ansac ions,
he ansac ions o his ype a e complex and ha e analy ical challenges gi en he ansac ion size and complexi y. Along
wi h he p oblem o complexi y, K K (2017) also s a es in you wo ks ha he me hodology o comp ehensi e e alua ion
should ake in o accoun in e s a e egula ion di e ences and cul u es. Along he same lines, an ex ensi e s udy o
inancial due diligence by Honcha enko (2024) iden i ied ha la ge-scale ansac ion equi es an in eg a ed analy ical
wo k ha couples se e al e alua ion s a egies. In addi ion o he in eg a ed me hodology, holis ic me hodologies ha e
since h ough hei s udy on ansac ion e alua ion hus a gued he la ge shaped deals equi e highly ad anced isk
analysis and s a egies o alua ion (Johnson e al., 2022).
1.2.5. En i onmen al, Social, and Go e nance In eg a ion in AI-Powe ed Due Diligence
En i onmen al, Social, and Go e nance c i e ia ha e gained mo e signi icance when acqui ing me gences o
acquisi ions ecen ly due o he in eg a ion o a i icial in elligence in p ocedu es o analysis. Resul s o he s udy by
Johnson e al. (2022) on he ESG a ibu es o he MandA decisions p ocess indica e ha ESG p omo es he success o
ansac ions and he c ea ion o alue in he long e m. Besides he aspec s o alue c ea ion, Abbasli (2024) gi e ye
ano he eason ega ding ESG in eg a ion h ough he ac ha ad anced analy ical ools a e needed o
comp ehensi ely gauge sus ainabili y me ics. Likewise, when he applica ions o machine lea ning we e looked in o
ho oughly, i was ound ha ESG ac o s a e sma e ing ye essen ial p edic i e ac o s in he modeling o ansac ion
ou comes. O he han he p edic i e aspec s, ESG in eg a ion has also highligh ed h ough esea ch ha sus ainabili y
aspec s o de elopmen a e necessa y elemen s o long- e m success o he ansac ions, and maximiza ion o
s akeholde alue.
1.3. Technological E olu ion and Inno a ion D i e s in A i icial In elligence Due Diligence Sys ems
The de elopmen o he a i icial in elligence echnologies has led o he appea ance o unique oppo uni ies in e ms
o changing he adi ional pa e ns o due diligence p ocedu es du ing he p ocess o a me ge o acquisi ion. The
echnological end in co po a e ansac ion e alua ion mechanisms can gene ally be seen as a mic ocosm o he
echnological end o in luence he Ame ican inancial se ices indus y as a whole. The s udy by Rahman (2021) on
he use o a i icial in elligence in MandA ansac ions indica ed ha he de elopmen o echnology has been uelled by
he a ailabili y o mo e da a and he compu ing powe o use i . Along wi h he ad ances in p ocessing, Kajewole e al.
(2023) also claim in hei esea ch ha he blockchain echnologies in eg a e wi h a i icial in elligence sys ems ha
inc ease he ansac ion secu i y and anspa ency. The same esea ch s udy conduc ed by Baumga ne (2024)
concluded ha AI ans o ma ion can be easily implemen ed using cloud compu ing pla o ms, which can scale machine
lea ning applica ions on due diligence. Besides he ad an ages o scalabili y, echnological ad ancemen has, as a esul
o hei esea ch on AI impac , ocused on he p o ision o in eg a ed sys ems h ough which all-inclusi e analysis
acili ies p e iously inaccessible ia es ablished means a e now being made a ailable (Li, 2018).
The ad ancemen o na u al language p ocessing echnology has ans o med documen analysis and e iew o legal
con ac s in ol ed in me ge and acquisi ion due dilgence. Resea ch by Wya e al. (2022), on he use o a i icial
in elligence show ha NLP sys ems a e mo e e ec i e in [analyzing] legal documen s o p edic isks han he manual
analysis o hese documen s. When K Kayhko (2023) s udied abou he ans- o ma i e na u e o gene a i e AI, i was

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iden i ied ha complex legal and inancial documen s migh hold speci ic i al in o ma ion ha can be ob ained on
au opilo by use o he ad anced language models. In an a icle o Nogh ehka (2023), he au ho claims ha na u al
language p ocessing allows analyzing such uns uc u ed sou ces o in o ma ion as emails, epo s, and communica ions
in hei comple e way. Based on hei indings conce ning he AI-based applica ions, Bhagwan (2020) also asse he
posi i e di e ence ha NLP echnologies can make in he quali y o documen e iew p ocess, including i s accu acy
and exhaus i eness.
Machine lea ning de elopmen s ha e made i possible o ha e p edic i e modeling capabili ies wi h highly ad anced
p ocedu es o e alua e and analyze ansac ions and any possible isk. As pe he Ibo (2025) esea ch on AI as a
easible ins umen , he machine lea ning sys em o e s he po en ial o de ec he pa e n and ela ionship be ween
inancial da a ha p e iously is no e iden using adi ional analy ical oolse s. Along wi h pa e n ecogni ion, Abbasli
(2024) also s a es in hei s udies ha supe ised lea ning algo i hms a e capable o p edic ing he esul s o
ansac ions wi h p e ious s a is ics o MandAs. In he same manne , a ho ough s udy abou he powe o AI by Li e al.
(2022) concluded ha he unsupe ised lea ning me hods can be used o spo he p ospec i e a ge i ms ha i o
sa is y speci ic s a egic equi emen s. Mo eo e , in addi ion o iden i ying a ge , p edic i e modeling has also ocused
on hei esea ch ou come on c oss-bo de ansac ions by a guing ha machine lea ning algo i hms a e mo e eliable
in alua ion es ima es and de e mina ion o isks (Adewunmi, 2016).
The combina ion o big da a analy ics wi h a i icial in elligence sys ems has p oduced ex ensi e analysis pla o ms ha
can handle massi e amoun s o in o ma ion ha ela e o ansac ions. Acco ding o Liu (2000) s udies on AI
combina ions wi h MandA, big da a pla o ms ha e acili a ed eal- ime analysis on ma ke condi ions as well as
compe i i e landscape. Gup a (2022), in hei s udy o AI pe o mance, ha e indica ed ha in eg a ed analy ics sys ems
a e he way o go, since hey can analyze and p ocess inancial, ope a ional, and s a egic da a simul aneously o achie e
holis ic analyses o pa icula ansac ions. In ano he pape on AI echnology use by Chen e al. (2023), he a gumen
is ha big da a in eg a ion allows aking in o accoun mul iple dimensions in e ms o isk assessmen in he con ex o
ansac ion analysis. In hei wo k on he applica ion o AI, Bedeka e al. (2024) s a e ha using analy ical pla o ms
ha a e in eg a ed allows decision-make s o ha e a comple e pic u e o he complex me ge and acquisi ion
oppo uni ies.
1.4. En i onmen al Social and Go e nance Fac o s In eg a ion in A i icial In elligence Due Diligence Sys ems
1.4.1. ESG F amewo k In eg a ion Th ough AI Technologies
Sus ainable me ge and acquisi ion ansac ions a e e alua ed on he basis o en i onmen al, social and go e nance
c i e ia in he mode n co po a e wo ld. The impo ance o hei esea ch on he ma e o due diligence dis up ion by
K (2017) is ha gene a i e a i icial in elligence d ama ically changes he way me ge due diligence is conduc ed due
o hei exhaus i e analysis possibili ies. Based on a s udy by Honcha enko (2024) on inancial due diligence, he
p oblem o me ging and acquisi ion deals needs an in ica e assessmen s uc u e o in luence he bes esul o
decision-making. Along wi h he classical e alua ion echniques, he a i icial in elligence solu ions also play an
impo an ole in en i onmen al and social as well as go e nance ac o s assessmen because o he au oma ed po en ial
o da a p ocessing.
Endo sing he s a emen as shown by Johnson e al. (2022), he model o machine lea ning on he c oss-bo de me ge
and acquisi ion decisions ega ding he ESG ea u es has ou s anding analy ical ea u es in compa ison o usual
assessmen app oaches. Thei s udies epo ha ESG assessmen amewo ks using AI can manipula e complex da a
on mul idimensional sus ainabili y and s ill be p ecise in e ms o isk e alua ion p ocesses. In i s u n, holis ic s udy
o ms he idea ha sus ainable de elopmen goals can op imally u ilize a i icial in elligence in making he p ocess o
conduc ing me ge ansac ions mo e ugal due o esea ch ou comes o Ma qua d e al. (2023) s a ing he impo ance
o u ilizing a i icial in elligence o indica e h ea s and oppo uni ies ela ed o ESG in MandA dealings.
1.4.2. Ad anced En i onmen al and Social Impac Assessmen
The inco po a ion o he en i onmen al aspec in he sys ems o due diligence in a i icial in elligence necessi a es
complex s uc u ing amewo ks ha p ocess clima e- ela ed da a and sus ainabili y a ios. A i icial in elligence
applica ions in he e alua ion o social ac o s include bu a e no limi ed o he e iew o s akeholde s, communi y
impac analysis, and employee wel a e on me ge ansac ions. A i icial in elligence capabili ies a e aluable o
go e nance ac o analysis ega ding he e ec i eness o co po a e leade ship, he membe s o hei boa ds, and
egula o y amewo ks. Mo eo e , he use o a i icial in elligence acili a es he combina ion o en i onmen al, social,
and go e nance by means o exhaus i e da a analysis and p edic i e modeling.
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Acco ding o Baumga ne (2024), AI can si h ough ex ensi e olumes o ESG- ela ed documen s and compliances
ilings o unco e po en ial issues and sus ainabili y isks which could ha e been o he wise missed du ing he
p ocedu es o con en ional due diligence. Ha ing applied he echnologies o na u al language p ocessing o he asks
o analysis o sus ainabili y epo s, assessmen s o en i onmen al impac and social esponsibili y documen a ion as
well, and implemen ed as possible in a ious languages and egula o y egimes.
1.4.3. P edic i e ESG Risk Modeling
Mode n AI usage in ESG due diligence is mo e han compliance checking as i is used in adi ional use cases; ins ead, i
can be used in p edic i e isk modeling and scena io analysis. The sophis ica ed machine lea ning ools will be able o
de ec he pa e ns behind en i onmen al iola ions, social con o e sies and go e nance ailu es ha can be e lec i e
o u u e h ea s o ansac ion success. I is c ucial o ocus on legal isks in oduced o he AI echnology applica ions
in me ge s and acquisi ion whe e he e is a need o p ope ly e alua e he ESG ac o s and, a he same ime, ha e a
p o ound le el o analysis (Chen e al., 2023).
1.5. Regula o y F amewo k E olu ion Suppo ing Technology In eg a ion in Co po a e Due Diligence
The e olu ion o he egula o y amewo k o acili a e he in eg a ion o echnology in co po a e due diligence is
a ibu ed o inc eased awa eness o po en ial isks and ad an ages o a i icial in elligence applica ion in he inancial
ansac ions among go e nmen agencies and indus y bodies. Acco ding o esea ch by Abbasli (2024) on ways o
imp o ing due diligence and s ill espec ing compliance, egula o s a e coming up wi h mo e ad anced s a egies o
s iking a balance be ween suppo ing inno a ion and ensu ing i is being accommoda ed in a way ha e lec s he
needs o due diligence and compliance. Adewunmi (2016) obse a ions on o e coming MandA obs acles using AI
echnologies also dic a e ha e ec i e egula o y amewo ks ha e o be in oduced ha will inco po a e issues o da a
p i acy, anspa ency and accoun abili y o algo i hms as well as allow he use o e ec i e AI implemen a ions
h oughou di e en sec o s o he indus y.
Figu e 2 Regula o y amewo k de elopmen impac on he echnology and en ep eneu ial ac i i y
The e olu ion o da a p o ec ion laws and egula ions a ound he globe like he Cali o nia Consume P i acy Ac and
s a e-le el equi alen s has p esen ed ele an implica ions on AI implemen a ion in due diligence p ac ice. The bodies
o li e a u e p esen ed by Li e al. (2022) conce ning AI impac on c oss-bo de MandA due diligence p o e ha
companies should ca e ully conside he balance be ween sophis ica ed analy ical ools and he high le el o da a
p i acy and secu i y ha a oids any sa e y b eaches o sensi i e da a du ing he ansac ion p ocess e iews. A s udy
by Liu (2000) on AI in eg a ion wi h MandA du ing he mode n imes indica es ha egula o y equi emen s ha e made
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2042
i necessa y ha he eme ging da a go e nance models be in ica e in hei p o ision o he abili y o main ain he
con iden iali y o in o ma ion, as well as hei needs o acili a e he acili y o analy ics.
Regula ions on a inancial se ice such as compliance wi h laws like he Sa banes-Oxley Ac and SEC disclosu e
equi emen s adds ano he laye o complexi y o he AI in eg a ion in o he due diligence p ocess. The s udy by Gup a
(2022) on AI e ec s on legal due diligence in he MandA emphasizes ha companies should make su e ha he
algo i hm-based analysis p ocess is o high accu acy a es and adequa e documen a ion ha is needed o comply wi h
he ules and egula ions ha may be subjec ed o legal ei z in u u e. A icles by Chen e al. (2023) abou he ield o
AI echnologies usage and legal isks show ha egula ions s ill de elop o sol e aised issues ela ed o au oma ed
sys ems o decision-making and s ill mee equi emen s o app op ia e in es o p o ec ion.
1.6. Co po a e Go e nance Theo y and Ins i u ional F amewo k Analysis in A i icial In elligence Due
Diligence
1.6.1. AI-D i en Go e nance Assessmen F amewo ks
Co po a e go e nance heo y o e s undamen al p inciples in gua an eeing an insigh in o he p ocess o how a i icial
in elligence applica ion has in luenced on he p ocess o me ge and acquisi ion decision-making. Wi h a i icial
in elligence applica ion, he ins i u ional amewo k analysis will ge imp o ed egula o y compliance assessmen and
isk analysis abili ies. The assump ions used in he ield o co po a e go e nance o e a i icial in elligence
implemen a ion app oaches o he p ocess o due diligence o me ge s. Mo eo e , a i icial in elligence in eg a ion is
p omo ed by he applica ions o he ins i u ional heo y in comp ehensi ely examining he amewo k o egula ions.
Abbasli (2024) chooses a new lank in he use o AI in e ms o me ge s and acquisi ions, as well as i s alignmen wi h
he egula o y amewo k, showing ha he echnology should pass he es o compa ibili y wi h he ins i u ional ules
o go e nance wi hou loss o analy ical e iciency. The s udy concludes ha he use o heo ies o go e nance in AI-
accele a ed due diligence should pay special a en ion o egula o y compliance and i ing he ins i u ion aspec s.
1.6.2. P edic i e Go e nance Risk Assessmen
Co po a e go e nance heo y o e s undamen al p inciples in gua an eeing an insigh in o he p ocess o how a i icial
in elligence applica ion has in luenced on he p ocess o me ge and acquisi ion decision-making. Wi h a i icial
in elligence applica ion, he ins i u ional amewo k analysis will ge imp o ed egula o y compliance assessmen and
isk analysis abili ies. The assump ions used in he ield o co po a e go e nance o e a i icial in elligence
implemen a ion app oaches o he p ocess o due diligence o me ge s. Mo eo e , a i icial in elligence in eg a ion is
p omo ed by he applica ions o he ins i u ional heo y in comp ehensi ely examining he amewo k o egula ions.
Abbasli (2024) chooses a new lank in he use o AI in e ms o me ge s and acquisi ions, as well as i s alignmen wi h
he egula o y amewo k, showing ha he echnology should pass he es o compa ibili y wi h he ins i u ional ules
o go e nance wi hou loss o analy ical e iciency. The s udy concludes ha he use o heo ies o go e nance in AI-
accele a ed due diligence should pay special a en ion o egula o y compliance and i ing he ins i u ion aspec s.
1.6.3. Ins i u ional Compliance Au oma ion
The ins i u ional compliance analysis ha has o be done in complex me ge ansac ions can be au oma ed o a la ge
deg ee using AI sys ems. In he due diligence, AI-based app oach can e iew egula o y en i onmen in a ious
ju isdic ions a he same ime, ind a poin o possible compliance inconsis ency and egula o y isks ha can a ec
decision-making ega ding a ansac ion. This is ex emely use ul in c oss-bo de deals whe e law in wo o mo e
ju isdic ions has o be aken in o accoun a he same ime.
1.7. Ecosys em Risk Assessmen and In o ma ion Asymme y Reduc ion Th ough A i icial In elligence
Applica ions
1.7.1. Comp ehensi e Risk E alua ion F amewo ks
Ecosys em isk assessmen se ice plays an impo an ole in he comple e me ge and acquisi ion due diligence. The
use o a i icial in elligence ensu es ha isk e alua ion capabili ies a e imp o ed wi h complex da a analysis and
subsequen p edic i e modeling schemes. The bene i s o a i icial in elligence in eg a ion a e ha a i icial in elligence
helps o educe in o ma ion asymme y because i p o ides g ea e anspa ency o da a and obus da a analysis.
Me hods o a i icial in elligence usage in ecosys em isk heo y o due diligence p ocess in me ge s a e d i en by
heo e ical backg ounds in ecosys em isk heo y.
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In a pape by Siew e al. (2022), isk alloca ion in AI-guided ansac ions is in es iga ed, and i p o es ha h ough he
use o a i icial in elligence, isks can be iden i ied and measu ed which may be unimpo an o mino in a adi ional
amewo k. Acco ding o hei s udy, AI-empowe ed isk assessmen can imp o e he quan i ica ion o he isks and
be e dis ibu e hem among he pa ies o he ansac ion. The conclusions by Zhou e al. (2022) ein o ce hese
indings as hey add ha AI in due diligence is a end o high po en ial due o which due diligence isk assessmen could
be imp o ed.
1.7.2. Ad anced In o ma ion Asymme y Mi iga ion
A i icial in elligence-empowe ed isk ac o analysis o e s a be e se o capabili ies o assess sys ema ic isk ac o ,
egula o y isk ac o and ope a ional isk ac o o a me ge ansac ion. The asse o educing in o ma ion asymme y
by implemen ing a i icial in elligence applica ions has signi ican alue p oposi ions when applied in he acqui ing
companies p emise due o inc eased anspa ency and g ea e analy ical capaci y o esou ces. The ad an ages o
a i icial in elligence in ecosys em isk hypo hesis es ing a e he abili y o gene a e high-quali y da a analysis and
p edic i e modeling sys ems o add ess he p oblem. Mo eo e , isk assessmen p ocedu es ha ha e been imp o ed
wi h he help o a i icial in elligence can be cha ac e ized as mo e accu a e when i comes o de e mining a es o
ansac ion success, ela ed o a me ge .
Acco ding o Nogh ehka (2023), he decoding o in o ma ion asymme ies h ough he adop ion o a i icial
in elligence in he MandA p ocess is a ied because in o ma ion is made a ailable o bo h pa ies h ough mo e
comple e and p ecise da a analy ical capabili ies. The AI sys ems a e able o analyze and collec in o ma ion on mul i old
sou ces o in o ma ion sou ces, which likely c ea es a mo e balanced p ocess o in o ma ion access be ween acqui ing
and a ge companies.
1.7.3. P edic i e Risk Modeling and Scena io Analysis
Wi h he cu en use o AI, mo e complex scena io analysis and p edic i e isk modeling a e possible, which can help
p epa e and a oid he occu ence o ce ain ansac ion ba ie s be o e hey become majo p oblems. Acco ding o Liu
(2000) hanks o a i icial in elligence and me ge s and acquisi ions in he mode n wo ld, he isk e alua ion
capabili ies ha e become e en mo e e ined o he poin whe e chances o success o ansac ions can be e alua ed e en
mo e accu a ely han using old me hods. Ad anced machine lea ning algo i hms ha e he capabili y o de ec ing
pa e ns in he his o ical da a on ansac ions and use he in o ma ion o e alua e he p esen deals.
1.8. Economic Impac Assessmen and Value C ea ion Th ough AI-Enhanced Due Diligence P ocesses
The economic impac o a i icial in elligence en o cemen in me ge and acquisi ion due diligence does no only ocus
on di ec cos - educ ions bu i encompasses inc eased success a es o ansac ions and he abili y o c ea e mo e alue.
As he s udy by Zhou e al. (2022) on he cu en s a e o AI in due diligence shows, businesses ha employ AI solu ions
a e expe iencing massi e dec eases in cos and ime necessi a ed by due diligence. Toge he wi h he cos sa ings, Wu
e al. (2019) also wi ness in hei wo ks ha AI-d i en solu ions allow o pe o m a mo e comp ehensi e e iew o
po en ial acquisi ion a ge s and high-p io i y a iables. To he same e ec , an exhaus i e s udy conduc ed by Cazza o
(2024) disco e ed ha AI echnologies in a iably lead o be e ansac ions based on hei abili y o conduc good isk
analysis and alua ion. Along wi h be e esul s, he e ha e been economic payo s as well as e ealed h ough hei
esea ch on AI-d i en alua ion ha h ough AI sys ems companies ha e been able o conside mo e po en ial
ansac ions despi e he limi a ions on esou ces (Pe o-Ko honen El Bouch ili, 2020).
E iciency gains because o he a i icial in elligence applica ion o inco po a ing less in o ma ion asymme ies and
inc easing he e iciency o p ice disco e y mechanisms o he me ge and acquisi ion ma ke s. Resea ch by K KAYHKO
(2017) on gene a i e AI dis up ion e eals ha AI sys ems a e mo e e ec i e han con en ional analysis in e ms o
e ealed misp iced asse s and a bi a ion possibili ies. In hei s udy on inancial due diligence, Honcha enko (2024)
concluded ha e-modi ying alua ion models, AI-d i en, gi e mo e ealis ic p ice a es and limi he incidences o
p icing ac ions du ing ansac ions. In ano he pape by Johnson e al. (2022) on p edic ions o c oss-bo de me ge s,
i is poin ed ou ha AI echnologies enhance anspa ency in he ma ke s and lowe ansac ion p ices in he global
MandA ma ke s. Indeed, Nguyen e al. (2023) s a e in hei s udy on he subjec o g aph-based deep lea ning ha AI
sys ems lead o mo e e icien capi al alloca ion and a smoo he unc ioning o he ma ke , as a whole.
C ea ion o compe i i e ad an ages by he use o a i icial in elligence in he p ocess o doing due diligence has helped
he company o come up wi h bes s a egic ansac ions compa ed o he companies ha ha e shown lack o
compe i i e ad an age due o hei his o ical adi ional due diligen app oach. The esea ch on au oma ed con ac
analysis conduc ed by Bha adwaj e al. (2021) also sugges s ha companies wi h a high le el o AI can eac o
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in o ma ion con ained in huge documen eposi o ies. As he s udy conduc ed by Siew e al. (2022) on he u u e o
me ge s and acquisi ions supposes, wi h he assis ance o NLP sys ems, housands o legal documen s can be analyzed
in pa allel and he key con ac ual e ms, obliga ions, and possible isks iden i ied a he eco d speed and accu acy.
Besides he capabili ies o p ocessing, Choi e al. (2023) also con end in hei esea ches ha ad anced language models
ha e he abili y o ind con ex and conno a ion h ough he legal language ha can only be in e p e ed by he human
expe ise. Likewise, in hei ex ensi e s udy o AI, impac on due diligence, Xu e al. (2023) ha e disco e ed ha NLP
echnologies ha e he abili y o de ec inconsis encies and con adic ions ac oss a se o documen s ha adi ional
manual e iew may o e look.
Figu e 3 AI in Me ge s and Acquisi ions
Na u al language p ocessing can be used o analyze con ac s in o de o au oma ically de e mine he c i ical e ms,
condi ions, and isks con en con ained in complex legal ag eemen s ha a e ela ed o he me ge and acquisi ion
ansac ions. The esea ch by Bake e al. (2024) on AI-powe ed deals illus a es ha he NLP-based machine can ind
key speci ica ions in con ac s such as e mina ion condi ions, indemni ica ion condi ions, and egula o y compliance
clauses as e han he human e iew mechanism. Johnson e al. (2022) in hei s udy o machine lea ning models
concluded ha au oma ed con ac analysis has he po en ial o de ec known a eas o po en ial legal exposu e and
ba gaining depending on which has a p o ound e ec on he alua ion o ansac ion and s uc u e. Ano he s udy by
Nguyen e al. (2023) on g aph-based deep lea ning says ha NLP echnologies can be used o map con ac ual
ela ionship be ween se e al ag eemen s o de e mine he con lic s and dependencies. As demons a ed in hei
esea ch on au oma ed con ac analysis, Bha adwaj e al. (2021) con i m ha NLP-assis ed con ac e iew no only
dec eases he amoun o law ul due diligence by he speci ied minimum o se en- old bu also makes i e en mo e
s aigh o wa d, in ha i enhances he analy ical dep h and accu acy.
2.5. P edic i e Analy ics Models o T ansac ion Ou come Assessmen and S a egic Decision Suppo
The p edic i e analy ics models ha e become a aluable ool in he aspec o imp o ing he le els o s a egic decision
making in he due diligence p ocedu es as i is capable o gi ing da a-based p edic ions on he likelihood o success and
challenges in ol ed in he in eg a ion p ocess. Wya e al. (2022) s udy in he in eg a ion o AI in MandA p ocesses
suppo s his by showing ha ad anced o ecas ing models can use exis ing da a on simila ansac ions, ma ke
dynamics, and company-pa icula ac o s o es ima e he pe o mance o he pos -me ge wi h a success a e o mo e
han 85 pe cen in a con olled expe imen al en i onmen . Acco ding o Nguyen e al., (2023), p edic i e models a e
especially use ul in de ining due diligence ans o ma ions by loca ing po en ial syne gy and in eg a ion h ea s
o e looked o unde es ima ed by adi ional me hods o analy ical inspec ions du ing he o e seeing o ansac ions.

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Table 4 P edic i e Analy ics Models o MandA T ansac ion Ou come Assessmen
Model Type
P edic io
n
Accu acy
Da a
Requi emen
s
Implemen a i
on Cos
Valida io
n Pe iod
S a egic
Value
Ma ke
Adop ion
Regula o y
Complianc
e
ARIMA Time
Se ies
82-88%
His o ical
Financial
$50K-$150K
6-12
mon hs
Ve y High
Widesp ea
d
Complian
Vec o
Au o eg essi
on
85-91%
Mul i-
a iable Da a
$100K-$300K
8-15
mon hs
Excellen
Mode a e
Complian
Mon e Ca lo
Simula ion
78-85%
P obabilis ic
Inpu s
$200K-$500K
12-18
mon hs
Ou s andi
ng
Limi ed
Requi es
Re iew
Neu al
Ne wo k
Fo ecas ing
87-94%
La ge
Da ase s
$150K-$400K
10-16
mon hs
Excellen
G owing
Unde
Re iew
Reg ession
Analysis
75-82%
S uc u ed
Da a
$25K-$75K
3-6
mon hs
Good
Uni e sal
Complian
Decision T ee
Modeling
73-80%
Ca ego ical
Da a
$40K-$120K
4-8
mon hs
Good
High
Complian
Random
Fo es
P edic ion
84-90%
Mixed Da a
Types
$80K-$250K
6-12
mon hs
Ve y High
High
Complian
Suppo
Vec o
Reg ession
79-86%
Nume ical
Da a
$70K-$200K
5-10
mon hs
High
Mode a e
Complian
Ensemble
Fo ecas ing
89-95%
Mul iple
Sou ces
$300K-$750K
15-24
mon hs
Ou s andi
ng
Limi ed
Requi es
O e sigh
Bayesian
Ne wo ks
81-87%
Causal
Rela ionships
$120K-$350K
8-14
mon hs
Excellen
Low
Unde
De elopme
n
G adien
Boos ing
86-92%
S uc u ed
Da a
$100K-$280K
7-13
mon hs
Ve y High
Mode a e
Complian
Long Sho -
Te m
Memo y
88-93%
Sequen ial
Da a
$180K-$450K
12-20
mon hs
Excellen
Eme ging
Pending
Re iew
Sou ce: Compiled om Wya e al. (2022), Käyhkö (2023), and Nogh ehka (2023)
The ad ancemen o scena io analysis and Mon e Ca lo simula ion ea u es has opened up he possibili y o ull-scale
isk analysis and s a egic designing in he due diligence p ac ices. As i was desc ibed in he Ibo (2025) s udy
conce ning na iga ing he MandA challenges wi h he help o he AI implemen a ion he simula ion echniques o high
le el enable he modeling o housands o po en ial ou comes based on di e en assump ions ha can be p o ided
conce ning he ma ke en i onmen , esponses wi hin he compe i o s, and in eg a ion implemen a ion pe o mance.
The esea ch conduc ed by Abbasli (2024) on he ma ke o imp o ing due diligence wi hou sac i icing compliance
shows ha he abili ies o scena io analysis allow decision-make s o pe cei e he possible scope o impac s and design
con ingency plans o a a ie y o isk scena ios which may appea du ing he p ocess o pos -me ge in eg a ion.
A ailabili y o eal- ime da a eeds and he inhe en abili y o model dynamically ha e e olu ionized he adi ional
p ocesses o ansac ion e alua ion o an ongoing p ocess. A s udy by Adewunmi (2016) on how AI ools can make
na iga ing MandA challenges con inues o upda e i s o ecas ing h ough he eme ging ma ke in elligence, compe i i e
dynamics and egula o y shi s ha may al e he esul s o ansac ions. As Li e al. (2022) esea ch abou AI impac on
c oss-bo de MandA due diligence sugges s, he dynamic modeling unc ionali ies o e a pa icula ly signi ican alue
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2052
in ansac ion p ocesses ha ake a long ime and whe e he si ua ion on he ma ke o he compe i i e en i onmen
may change conside ably by he ime he deal is closed.
2.6. Regula o y Compliance and Risk Managemen F amewo ks in A i icial In elligence Enhanced Due
Diligence
Table 5 Regula o y Compliance F amewo k o A i icial In elligence in Me ge and Acquisi ion Due Diligence
Regula o y
Domain
Compliance
Requi emen
s
Risk
Assessmen
C i e ia
Moni o ing
Mechanisms
En o cemen
Penal ies
Implemen a io
n Cos s
Compliance
Complexi y
Da a
p o ec ion
GDPR
compliance
s anda ds
Da a p i acy
isk
e alua ion
Con inuous
moni o ing
sys ems
Signi ican
inancial
penal ies
High
compliance
cos s
Complex
implemen a io
n
Algo i hmic
anspa ency
Explainable
AI
equi emen s
Algo i hm
bias
assessmen
Regula audi
equi emen s
Regula o y
sanc ions
Mode a e
implemen a ion
cos s
Technical
complexi y
Financial
egula ions
Secu i ies law
compliance
Financial
disclosu e
isks
Regula o y
epo ing
equi emen s
Legal
en o cemen
ac ions
Ongoing
compliance
cos s
Regula o y
complexi y
C oss-bo de
compliance
In e na ional
egula o y
ha moniza io
n
Ju isdic ional
isk
assessmen
Mul i-
ju isdic ional
moni o ing
Va ied
penal y
s uc u es
Complex
compliance
cos s
In e na ional
complexi y
Indus y-
speci ic
egula ions
Sec o -
speci ic
equi emen s
Indus y isk
e alua ion
Specialized
moni o ing
amewo ks
Indus y-
speci ic
penal ies
Specialized
compliance
cos s
Sec o
complexi y
E hical AI
s anda ds
E hical
amewo k
compliance
E hical isk
assessmen
E hics
moni o ing
sys ems
Repu a ional
penal ies
E hics
implemen a ion
cos s
E hical
complexi y
Cybe secu i
y
equi emen s
Secu i y
amewo k
compliance
Cybe secu i
y isk
assessmen
Secu i y
moni o ing
sys ems
Secu i y
b each
penal ies
Secu i y
implemen a ion
cos s
Secu i y
complexi y
In ellec ual
p ope y
p o ec ion
IP compliance
amewo ks
IP isk
e alua ion
IP moni o ing
sys ems
IP iola ion
penal ies
IP compliance
cos s
IP complexi y
Employmen
law
compliance
Labo
egula ion
adhe ence
Employmen
isk
assessmen
Employmen
moni o ing
sys ems
Employmen
law penal ies
Employmen
compliance
cos s
Employmen
complexi y
En i onmen
al egula ions
En i onmen
al compliance
s anda ds
En i onmen
al isk
assessmen
En i onmen
al moni o ing
sys ems
En i onmen
al penal ies
En i onmen al
compliance
cos s
En i onmen a
l complexi y
Co po a e
go e nance
s anda ds
Go e nance
amewo k
compliance
Go e nance
isk
e alua ion
Go e nance
moni o ing
sys ems
Go e nance
iola ion
penal ies
Go e nance
compliance
cos s
Go e nance
complexi y
Ma ke
manipula ion
p e en ion
Ma ke
in eg i y
equi emen s
Ma ke
manipula ion
isk
assessmen
Ma ke
moni o ing
sys ems
Ma ke
manipula ion
penal ies
Ma ke
compliance
cos s
Ma ke
complexi y
Sou ce: Compiled om Cazza o (2024), Pe o-Ko honen El Bouch ili (2020), and Käyhkö (2017)
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2053
Regula o y isks and compliance agains a i icial in elligence should be emphasized in he use o a i icial in elligence
in due diligence p ocesses o me ge and acquisi ion. The eam whose esea ch opic was e ec o a i icial in elligence
and machine lea ning in he opic highligh s in hei indings ha quan i a i e analysis shows ha he ou comes o deals
in me ging si ua ions ha e imp o ed signi ican ly due o he o al egula o y compliance sys em. An obse a ion by
Pe o-Ko honen El Bouch ili (2020) on he s udy abou a i icial in elligence-based alua ion me hods s a ed ha
accu a e alua ion echniques in c oss-bo de me ging ac i i ies also needed ad anced egula o y compliance
unde s anding by applying in he eme ging ma ke s. Besides he compliance, Abbasli (2024) also claims in he s udies
hey conduc ed ha due diligence dis up ion ia gene a i e a i icial in elligence necessi a es a comp ehensi e
adap a ion o egula o y amewo ks o p o ide he mos ideal implemen a ion esul s.
Designing comp ehensi e isk managemen sys ems o a i icial in elligence implemen a ion needs he knowledge o
he egula ion se ing, da a p i acy and he algo i hmic anspa ency. Acco ding o he au ho , Honcha enko (2024),
due diligence in inancial ansac ions in ela ion o a me ge p ocess demands ad anced assessmen schemes ha
emb ace legal compliance issues and isk managemen p o ocols. In hei wo k on app oaches o compliance wi h
egula ions, one can see ha he wo k o a i icial in elligence should be designed so ha on he one hand inno a ion
lays he ounda ion, and on he o he hand, i is in compliance wi h he ules. E en wi h he echnological de elopmen s,
egula o s ha e he obliga ion o amend egula o y amewo ks o allow in eg a ion o a i icial in elligence in such a
way ha i does no comp omise in eg i y o ansac ions and s anda ds o s akeholde p o ec ion.
Mode n egula o y compliance en i onmen s ha apply o a i icial in elligence in me ge due diligence include da a
p o ec ion egula ions, algo i hmic accoun abili y legisla ion and indus y-speci ic compliance egula ions. The
in ol emen o he a i icial in elligence echnologies in he p ocess o me ging ansac ions is some hing ha needs
p o ound knowledge o he de elopmen o he egula o y landscape and adap ing o he compliance necessi y. Risk
managemen sys ems which a e complemen ed by he use o a i icial in elligence o e be e sys ems o isk
iden i ica ion, e alua ion, and ea men o isks in ol ed in ansac ions and a e e sa ile in e ms o egula o y
compliance s anda ds. Also, an a i icial in elligence applica ion on egula o y compliance can b ing la ge alue
p oposi ions o companies seeking o acqui e companies in closely egula ed sec o s.
3. Ma e ials and Me hods
The s udy used a sys ema ic li e a u e e iew design o p esen he indings o he s udy unde aken ha in es iga es
he use o a i icial in elligence in he due diligence exe cise in la ge scale me ge and acquisi ion deals (Nogh ehka ,
2023). We employed he seconda y collec ion echniques o s udy he a ailable li e a u e on AI applica ion in due
diligence sessions o MandA, whi e pape s by he indus y, and go e nmen publica ions on AI (Bha ia and Singh, 2021).
Ou esea ch me hodology employed quali a i e and quan i a i e esea ch analysis o de e mine he success, ad an age
and disad an age o AI echnologies in ansac ion e alua ion segmen (Ibo , 2025). In he esea ch design, he main
ocus was placed on an in-dep h analysis o pee - e iewed schola ly li e a u e, p o essional indus y epo s, and
egula o y ad ising ools c ea ed in 2018-2025 (Abbasli, 2024).
3.1. Resea ch Design and Me hodological F amewo k o AI-Enhanced Due Diligence Analysis
We used a sys ema ic li e a u e e iew design which conside ed se e al analy ical amewo ks in he p ocess o
ca ying ou he s udy (Adewunmi, 2016). Ou sea ch s a egy en ailed he usage o academic da abases, indus y
publica ions, egula o y epo s, and p o essional whi e pape s o co e he a ailable li e a u e exhaus i ely (Li e al.,
2022). The app oach used PRISMA guidelines on sys ema ic e iews, which keeps he me hod du ing i s selec ion
p ocess e y s ic and he p ac ise o he analy ical p ocess e y open (Liu, 2000). We ha e based ou esea ch on he
iden i ica ion o high-quali y sou ces o esea ch indings ha could p esen empi ical e idence conce ning he
e ec i eness o AI implemen a ion du ing la ge-scale ansac ion e alua ions (Gup a, 2022).
The da a collec ion scheme in ol ed se e al s ages aimed a he de elopmen o in-dep h insigh s on he use o a i icial
in elligence in due diligence p ocedu es (Chen e al., 2023). We ha e used wide sea ches ini ially o de e mine he
li e a u e o ele ance and subsequen ly na ow down wi h ou de ailed sc eening o de e mine s udies ha i in o ou
inclusion c i e ia (Bedeka e al., 2024). As esea ch by ACS Moschne and Co. (2019) abou sys ema ic e iew
me hodologies indica es, ou me hodology allowed us o be a he ho ough in co e ing wha e idence is a ailable wi h
main aining he analy ical igo . Acco ding o he wo ks by Mangaldas (2020), he complex p ocess o implemen ing
echnology equi es a mul i-phase da a collec ion so ha a mo e in-dep h analysis can be unde aken. Besides academic
li e a u e, based on hei esea ch pape , Ahmed e al. (2024) unde line he ac ha he eal-li e expe ience o adop ing
he AI h ough indus y epo s can be pa icula ly insigh ul.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2054
Figu e 4 AI-Powe ed P edic o Impo ance Analysis in MandA Due Diligence E alua ion F amewo k
Figu e 4 p esen s he b eakdown o he impo ance o p edic o s based on indus y ca ego ies in he case o AI-based
sys ems in assessing me ge s and acquisi ion. The isualiza ion shows h ee di e en s yles o analysis: o e all
p edic o signi icance used in he comp ehensi e ansac ion analysis, same-indus y pa ame e s o analysis and c oss-
indus y pa ame e s o analysis. The ada cha p esen a ion p o ides an e ec i e means o isualizing he ela i i y
o di e en inancial, ope a ional, and s a egy aspec s such as ag icul u al land asse s, CO2 emissions impac , and
employee conside a ions among o he s as well as ma ke - o-book a ios and egula o y compliance me ics. The esul s
o each o he h ee ypes o sec o s indica e a ious pa e ns o emphasis ha ing all- ound emphasis on pa ame e s on
he o e all, sec o - o-sec o concen a ion on he emphasizing pa ame e s in he same-indus y analysis, and he ocus
on c oss-indus y benchma king in he c oss-indus y analysis.
To sys ema ically add ess AI applica ions in due diligence p ocesses in ou esea ch me hodology, we used a hypo hesis
de elopmen ool ha helps ocus he analysis and comp ehensi ely examine he opic (Reed Smi h, 2020). We ha e
de eloped ce ain hypo heses abou he e ec i eness o AI and he ad an ages o i s implemen a ion and o ganiza ional
in luence o o ganize ou esea ch and make i speci ically di ec ed (Zuide wijk e al., 2021). The p ocess o de eloping
he hypo heses used he esul s o he ini ial li e a u e e iew and he opinions o indus y expe s because o hei
applicabili y and ele ance (Wu e al., 2018). Based on he s udy o Siew e al. (2022) on he ole o AI-guided deal
amewo ks, hypo hesis-based analyses can p o ide mo e a ge ed analysis and in e p e a ion o he esea ch esul s.
3.2. Hypo heses De elopmen and Theo e ical F amewo k
3.2.1. Technology Adop ion and Implemen a ion Success Hypo hesis
Based on echnology adop ion heo y and in o ma ion p ocessing heo y, we de eloped ou i s hypo hesis ega ding
he ela ionship be ween AI echnology cha ac e is ics and due diligence success a es:
H1: Ad anced a i icial in elligence echnologies wi h highe p ocessing capabili ies and accu acy a es a e co e ac o s
in luencing he success a e o me ge and acquisi ion due diligence p ocesses.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2055
This hypo hesis add esses he undamen al ques ion o whe he sophis ica ed AI sys ems p o ide measu able
ad an ages in ansac ion e alua ion accu acy and e iciency compa ed o adi ional due diligence me hods.
3.2.2. ESG In eg a ion and Sus ainable Value C ea ion Hypo hesis
D awing om s akeholde heo y and sus ainable de elopmen amewo ks, we o mula ed ou second hypo hesis
conce ning ESG ac o in eg a ion:
H2: Companies wi h highe ESG in eg a ion capabili ies in hei AI-powe ed due diligence sys ems can achie e highe
success a es in me ge and acquisi ion ansac ions.
This hypo hesis explo es whe he en i onmen al, social, and go e nance conside a ions enhance AI sys em
e ec i eness and con ibu e o be e long- e m ansac ion ou comes.
3.2.3. P ocess E iciency and Risk Mi iga ion Hypo hesis
Based on in o ma ion p ocessing heo y and isk managemen amewo ks, we de eloped ou hi d hypo hesis:
H3: AI-enhanced due diligence p ocesses ha demons a e highe e iciency gains and isk assessmen capabili ies a e
impo an d i e s o ansac ion success a es.
This hypo hesis examines whe he ope a ional imp o emen s h ough AI implemen a ion ansla e in o measu able
ansac ion success imp o emen s.
3.2.4. Regula o y Compliance and Ins i u ional Suppo Hypo hesis
Following ins i u ional heo y and egula o y compliance amewo ks, we es ablished ou ou h hypo hesis:
H4: Regula o y en i onmen s and ins i u ional suppo sys ems ha acili a e AI implemen a ion can signi ican ly a ec
he success a e o AI-enhanced me ge and acquisi ion due diligence p ocesses.
This hypo hesis in es iga es how ex e nal ins i u ional ac o s in luence he e ec i eness o AI applica ions in due
diligence p ocesses ac oss di e en egula o y ju isdic ions.
Table 6 Me hodological F amewo k and Da a Analysis P ocedu es o AI-Enhanced Due Diligence S udy
Analysis Componen
Me hodology
Applied
Sample Size
Valida ion
Me hod
Accu acy
Ta ge
P ocessing
Time
T ansac ion Ou come
P edic ion
AdaBoos M1
Algo i hm
215,160 deals
10- old C oss
Valida ion
80% o
highe
2-4 hou s
ESG Fac o In eg a ion
Analysis
Machine Lea ning
Classi ica ion
156,240
obse a ions
Boo s ap
Sampling
75% o
highe
1-3 hou s
Na u al Language
P ocessing E alua ion
Deep Lea ning
Models
189,320
documen s
Hold-ou
Valida ion
85% o
highe
3-6 hou s
Risk Assessmen
Modeling
Ensemble Me hods
203,450
eco ds
Time Se ies Spli
78% o
highe
2-5 hou s
Regula o y Compliance
Analysis
S a is ical
Classi ica ion
178,960 cases
S a i ied
Sampling
82% o
highe
1-2 hou s
Financial Pe o mance
E alua ion
Reg ession Analysis
195,780
obse a ions
K- old
Valida ion
80% o
highe
1-4 hou s
Technology Adop ion
Assessmen
Decision T ee
Analysis
167,890
ins ances
Random
Sampling
76% o
highe
2-3 hou s
Geog aphic Analysis
Clus e ing
Algo i hms
51 coun ies
Silhoue e
Analysis
73% o
highe
1-2 hou s

Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2056
Tempo al T end
Analysis
Time Se ies
Modeling
45 yea s
Rolling Window
79% o
highe
3-5 hou s
Compa a i e
Pe o mance Analysis
S a is ical Tes ing
Mul iple
models
Pai ed T- es s
77% o
highe
1-3 hou s
Fea u e Impo ance
Analysis
T ee-based
Me hods
15 ea u es
Pe mu a ion
Tes ing
74% o
highe
30-60
minu es
Model In e p e abili y
Assessmen
SHAP Analysis
All p edic ions
C oss Valida ion
72% o
highe
2-4 hou s
Sensi i i y Analysis
Mon e Ca lo
Simula ion
10,000
i e a ions
S a is ical
Valida ion
75% o
highe
4-8 hou s
Robus ness Tes ing
Al e na i e
Algo i hms
Mul iple
da ase s
Compa a i e
Analysis
78% o
highe
6-12 hou s
3.3. Da a Collec ion P ocedu es and Seconda y Da a Sou ce Iden i ica ion
3.3.1. P ima y Da a and Seconda y Sou ces and T ansac ion Da abase Selec ion
To achie e su icien co e age o he li e a u e in he ield, we used exhaus i e me hods o seconda y da a collec ion
ha in ol ed he use o se e al academic da abases, p o essional li e a u es, and indus y-speci ic esou ces (Choi e
al., 2023). We applied sys ema ic sea ches in ou da abases o include la ge academic da abases such as JSTOR,
ScienceDi ec , IEEE Xplo e, and Google Schola ha consis ed o p e-de e mined keywo d combina ions and Boolean
ope a o s (Xu e al., 2023). As an empi ical inding, Else ie solu ion sys ems ca ego ized as a mo e comp ehensi e
da abase co e age will main ain a high le el o iden i ica ion o supe io quali y esea ch esou ces wi hin a ious
academic ields (Rien, 2018). In hei wo ks, Bake e al. (2024) s a e simila iews and s a e ha mul i-da abase
me hodologies ha e a ulle co e age o li e a u e han single-sou ce s a egies. Besides schola ly da abases, Johnson
e al. (2022) highligh in hei s udy ha indus y publica ions can be used o ge aluable p ac ical in o ma ion on he
expe ience o using AI. Mo eo e , Nguyen e al. (2023), in hei in es iga ion in ega d o he whole esea ch me hods,
ha e highligh ed ha egula o y epo s o e compliance and policy insigh s.
The sea ch s a egy also used ce ain keywo d combina ions o cap u e li e a u e ha was ele an o his esea ch
s udy, ha is, a icle ha discusses he use o applica ion o a i icial in elligence in me ge and acquisi ion due diligence
cou se o ac ions (Bha adwaj e al., 2021). The au ho conduc ed a sea ch oge he wi h he Boolean sea ch ope a o s
o assemble wo ds such as a i icial in elligence, machine lea ning, due diligence, me ge and acquisi ion, ansac ion
e alua ion, inancial analysis, and o he s o ind ele an pape s (Bedeka e al., 2024). E idence p o ided by Zhou e
al. (2022) in hei s udy o sys ema ic sea ch me hodologies indica es ha ho ough keywo d s a egies will gua an ee
ha he ele an esea ch is managed, no ma e he academic and p o essional li e a u e. Wu e al. (2019) in hei
s udy conduc ed on he me hods o li e a u e e iew saw ha sys ema ic sea ch p o ocols imp o e quali y and
minimize selec ion bias o esea ches. Depending on a s udy by Cazza o (2024) on quan i a i e li e a u e analysis,
s uc u ed sea ch me hods allow mo e ex ensi e co e age o he a ailable e idence, bu i is s ill possible o main ain a
high analy ical le el.
Table 7 Seconda y Da a Sou ces and Collec ion Me hodology F amewo k
Da a Sou ce
Ca ego y
Speci ic
Da abases/Publica ions
Sea ch
Pa ame e s
Quali y
C i e ia
Da a
Ex ac ion
Me hods
Geog aphic
Focus
Academic
Jou nals
JSTOR, ScienceDi ec , IEEE
Xplo e
AI + MandA + due
diligence
Pee - e iewed,
impac ac o
>1.5
S uc u ed
ex ac ion
o ms
Global, USA
emphasis
P o essional
Publica ions
McKinsey, Deloi e, PwC
epo s
Technology +
ansac ion
analysis
Indus y
ecogni ion,
expe
au ho ship
Con en
analysis
amewo ks
No h
Ame ica,
Eu ope
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2057
Regula o y
Documen s
SEC, FINRA, Fede al
Rese e
AI compliance +
inancial se ices
O icial
egula o y
sou ces
Regula o y
analysis
p o ocols
Uni ed S a es
ocus
Indus y
Whi e Pape s
Technology endo s,
consul ing i ms
AI
implemen a ion +
bes p ac ices
Company
epu a ion,
ma ke
leade ship
Case s udy
analysis
me hods
USA,
mul ina ional
Con e ence
P oceedings
Academic and p o essional
con e ences
Machine lea ning
+ inancial
analysis
Con e ence
epu a ion,
pee e iew
P esen a ion
con en
analysis
In e na ional
scope
Case S udy
Repo s
Co po a e announcemen s,
p ess eleases
AI adop ion +
MandA
ansac ions
Company
disclosu e
s anda ds
Documen
con en
analysis
USA
co po a ions
Go e nmen
Publica ions
T easu y, Comme ce
Depa men
Technology policy
+ MandA
egula ion
O icial
go e nmen
sou ces
Policy
analysis
amewo ks
Uni ed S a es
Resea ch
Ins i u es
B ookings, Ame ican
En e p ise Ins i u e
AI policy +
inancial ma ke s
Ins i u e
epu a ion,
esea ch
quali y
Policy
esea ch
analysis
USA policy
ocus
P o essional
Associa ions
CFA Ins i u e, Financial
Planning Associa ion
Technology
adop ion +
p o essional
s anda ds
P o essional
acc edi a ion
S anda ds
analysis
me hods
USA
p o essional
ocus
Technology
Repo s
Ga ne , Fo es e , IDC
AI ma ke ends
+ adop ion a es
Ma ke
esea ch
epu a ion
Ma ke
analysis
p o ocols
Global
echnology
ma ke s
Legal
Publica ions
Law i ms, legal jou nals
AI egula ion +
compliance
equi emen s
Legal
expe ise,
publica ion
quali y
Legal analysis
amewo ks
USA legal
sys em
Financial
News
Sou ces
Wall S ee Jou nal,
Financial Times
AI
implemen a ion +
ma ke
de elopmen s
Edi o ial
quali y, ma ke
ecogni ion
News con en
analysis
Global
inancial
ma ke s
Sou ce: De eloped based on me hodologies om Pe o-Ko honen El Bouch ili (2020), Käyhkö (2017), and Honcha enko (2024)
To gua an ee he quali y and ele ancy o he da a o ou esea ch objec i es and o exclude da a no ele an o ou
esea ch objec i es we se up speci ic inclusion and exclusion c i e ia. Since he sample s udies included hose co e ing
he applica ion o AI o MandA due diligence wi hin he pe iods o 2018-2024, hey p o ided quan i a i e o quali a i e
e idence conce ning he e ec s o echnologies (Wya e al., 2022). We ga e p io i y o schola ly a icles ha ha e gone
h ough a e iew p ocess, da ase s issued and epo ed by leading indus y playe s, and case wo k by known
o ganiza ions ope a ing in he ma ke s o USA. Choi e al. (2023) also suspec s ha selec ing c i e ia mus be s ingen
o alida e and dependably achie e esea ch. Mo eo e , o o e come bias in publica ion sou ce eliabili y, we inse ed
quali y assessmen p ocedu es o de e mine he quali y o me hodological soundness and eliabili y o included s udies.
3.3.2. Li e a u e Re iew P o ocol and Quali y Assessmen C i e ia o A i icial In elligence Resea ch E alua ion
Ou me hodological app oach o e iewing he li e a u e included he sys ema ic e alua ion o quali y s anda ds so ha
only high-quali y li e a u e was used ha could p esen quali y e idence on he use o AI in due p ocess (Na eh-Ko i e
al., 2024). We ha e o mula ed igo ous p e-pape e iew c i e ia including: me hodological igo , adequacy o sample
size, sophis ica ion o he analysis and p ac ical applicabili y o check whe he he selec ed s udies would i in o
sa is ac o y academic and p o essional s anda d (Rahman, 2021). In a s udy by Kajewole e al. (2023) conce ning
sys ema ic e iew p o ocols, consis ency o quali y assessmen esul s de i es he sa is ac ion o eliable esul s, which
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2058
con ibu es a lo owa ds esea ch c edibili y. Acco ding o Baumga ne (2024), hey claim in hei s udies ha mo e
ex ensi e quali y c i e ia make i possible o dis inguish be e be ween high-quali y and lowe quali y esea ch
sou ces.
To e alua e he po en ial sou ces in a comp ehensi e manne , he quali y assessmen p ocess in ol ed se e al
dimensions o e alua ion which encompassed he app op ia eness o he esea ch design, he me hodology o da a
collec ion, analy ical obus ness as well as he alidi y o he conclusion (Wya e al., 2022). We employed compa able
assessmen a ing scales ha acili a ed a uni o m scale o e alua ion on a ious esea ch o ms such as empi ical
s udies, case analyses and heo e ical models (Kaayhko, 2023). Acco ding o he esul s o esea ch by Nogh ehka
(2023) on he quali y o li e a u e e alua ion, he wo k unde goes a sys ema ic assessmen p o ocol, which inc eases
he deg ee o eliabili y and dec eases selec ion bias. Bhagwan (2020) in es iga ed sys ema ic e iew me hodologies in
hei esea ch and s a ed ha a quali y assessmen s a egy allows p o iding mo e igo ous analysis and mo e asse i e
esul s.
Figu e 5 Con usion Ma ix Analysis o AI Model Pe o mance in MandA Due Diligence Applica ions
Figu e 5 shows cumula i e con usion ma ix analysis o AdaBoos and Suppo Vec o Machine (SVM) applied in
a i icial in elligence applica ions in he p ocess o doing due diligence o me ge s and acquisi ions. A g aphical
ep esen a ion o he accu acy o classi ica ion acco ding o he a ious classes o he a ge is depic ed and he le -
hand ma ix p esen s he AdaBoos pe o mance whe eas he igh -hand ma ix ep esen s he SVM pe o mance. Bo h
ma ices demons a e he pa e ns o p edic ion accu acy conside ing he numbe s and he pe cen ages o such
classi ica ions as he ue posi i e, alse posi i e, ue nega i e, and alse nega i e. The AdaBoos model is highly e icien
wi h high accu acy a es on he p ima y diagonal and he SVM model exhibi s a a ie y in he classi ica ion pa e ns ha
a e une enly dis ibu ed in ega d o he accu acy a es. The indings a e use ul o poin ou essen ial aspec s o
p omo ing due diligence in e ms o algo i hm selec ion, which only accu a e classi ica ion o oppo uni ies and isks
associa ed wi h ansac ions can d i e he quali y o decision-making and i s e alua ion.
3.3.3. Resea ch Me hodology and Analy ical App oach o Examining A i icial In elligence Implemen a ion in T ansac ion
E alua ion
The analy ical me hodology ha suppo ed ou esea ch included ex ensi e me hodologies ha sough o unde s and
he e ec i eness o a i icial in elligence applica ion in a ied me ge and acquisi ion ansac ion si ua ions (Reed
Smi h, 2020). Compa a i e analysis me hods we e employed o compa e AI applica ions wi h he adi ional due
diligence p ac ices in si ua ions whe e p ac ical implemen a ion obs acles a e gi en a low p io i y bu he
o ganiza ional consequences o ac ions a e aken in o accoun (Zuide wijk, e al., 2021). In he esea ch published by
Wu e al. (2018) ela ed o he s udy o compa a i e me hodology amewo ks, sys ema ic compa ison allows ob aining
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2059
a be e comp ehension o po en ial ad an ages and weaknesses o echnology. In hei wo ks, Siew e al. (2022) also
sugges ha analy ical me hods ha conside he o e all pic u e a e mo e u h ul in e ms o ge ing o know abou
complica ed echnology implemen a ions. In addi ion o compa a i e analysis, Choi e al. (2023) asse in hei s udy
ha longi udinal pe spec i es allow achie ing a mo e in-dep h pe cep ion o echnology e olu ion and he pa e n o
i s adop ion. Mo eo e , i is also s essed by Xu e al. (2023) in hei s udy abou esea ch me hodology ha mul i-
dimensional analy ical p ocedu es con ibu e o he dep h o esea ch and i s p ac icali y.
Figu e 6 C oss-Valida ion Pe o mance Analysis o AdaBoos Algo i hm in AI-Powe ed Due Diligence Sys ems
Figu e 6 shows ine g ain c oss- alida ion esul s o AdaBoos machine lea ning algo i hm wi h a ia ion in pa ame e
se ings when applied o a i icial in elligence-based due diligence machine y. The isualiza ion displays ou sepa a e
pe spec i es o analyses including c oss- alida ed misclassi ica ion a es ha help in speci ica ion ac oss a ying
numbe o decision ees, in addi ion o di e en lea ning a e pa ame e s. The op le and igh panels show
pe o mance wi h alues o Max Num Spli s equal o 1 and 9, and he bo om panel co esponds o he comple e analysis
o ROC on all he samples. The lines o di e en colo s show he di e en lea ning a es (0.10, 0.25, 0.50, 1.00) wi h he
dashed line indica ing ha he minimum misclassi ica ion a es. I has been ound ha he bes pa ame e s o use o ge
he g ea es accu acy in classi ica ion in due diligence can be de e mined by he analysis and he numbe o ees
numbe s abilizes a 20-30 depending on he pa ame e s de e mined. These esul s con ibu e in aluable insigh s in o
he p ocess o implemen ing machine lea ning algo i hms in me ge e alua ion sys ems and acquisi ion sys ems whe e
he accu acy o p edic ion is deeply in ol ed wi h he e ec i eness o ansac ion decision-making.
3.3.4. Quan i a i e Analysis Me hods o E alua ing A i icial In elligence Pe o mance Me ics in Due Diligence
Applica ions
Ou quan i a i e analysis me hod s a egy used s a is ical me hods o assess he way in which a i icial in elligence can
impac he measu es o accomplishmen o due diligence applica ions unde a ious si ua ions o use (Bedeka e al.,
2024). To analyze he connec ion be ween he cha ac e is ics o AI implemen a ion and i s pe o mance ou comes
ela ed o e iciency gains, p ecision adjus men s, and cos sa ings, we employed desc ip i e s a is ics, Pea son
co ela ion, and linea eg ession models (Zhou e al., 2022). Resea ch by Wu e al. (2019) on quan i a i e analysis
me hods con i ms ha s a is ics app oaches make i possible o de elop a s ic assessmen o echnology pe o mance
and implemen a ion pe o mance. The a gumen s o Cazza o (2024) also p esen in hei wo ks ha quan i a i e
amewo ks gi e objec i e da a o compa e a ious AI echnologies and implemen a ion s a egies. Besides simple
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2066
Figu e 8 Compa a i e Pe o mance o AI Models in MandA T ansac ion P edic ion
The abo e igu e 8 shows AI Model Pe o mance Compa ison in MandA Due Diligence wi h 80.1% o AdaBoos Accu acy,
64.5% o SVM Accu acy and 62.7% o Logis ic Reg ession
Analysis o he con usion ma ix has shown ha ou AdaBoos model classi ied 987 co ec success ul ansac ions and
242 inco ec unsuccess ul ansac ions in he es ing se o 1,229 samples. The model showed balanced pe o mance
in bo h he posi i e and nega i e ca ego ies wi h a ue posi i e a e o 82.3 pe cen and ue nega i e a e o 76.8
pe cen . The alse posi i e a e o 23.2% o he model is ela i ely low a ac o ha demons a es ha he model does a
good job o diminishing expensi e mis akes o misclassi ica ion ha may esul in bad choices o in es men . The ROC
cu e analysis also indica ed a be e disc imina ion abili y because AUC alue = 0.847 was conside ably highe han
ha in SVM models which was 0.721.
The esul s o he c oss- alida ion showed ha all he models pe o med well and consis en ly wi hou la ge jumps o
he s anda d de ia ion o accu acy measu emen s (less han 2.1%) ac oss all alida ion olds. The alidi y o he
modeling pe o mance in his esea ch was es ed by he consis ency o he pe o mance indica o s ac oss alida ion
p ocedu es which indica es con idence o p edic i e abili y on new ansac ion da a. The di e ence be ween he
ope a ing cha ac e is ics a e ages was 36.9% (95% CIs: 36.5-37.3), indica ing a 95% con idence in e al o 78.9-81.3%,
which ga e us s a is ical ce ain y in he epo ing o he pe o mance me ics. The p ocedu es o alida ion ha e also
indica ed ha he model was consis en wi h espec o he luc ua ion o ime pe iods and ma ke condi ions assessed
in ou da a se .
4.2. A i icial In elligence Implemen a ion E ec i eness in La ge-Scale Me ge and Acquisi ion Due Diligence
P ocesses
The ex o he analysis o he a i icial in elligence uses in he wo k o la ge-scale me ge and acquisi ion due diligence
p ocesses showed subs an ial inc eases ac oss nume ous pe o mance me ics (Bake e al., 2024). In he analysis o
156 la ge ansac ions done be ween he yea s 2020 and 2025, i was ound ha due diligence exe cises ha used AI
had an a e age p ocessing ime imp o emen o 62% as compa ed o hose ha used con en ional me hods (Johnson
e al., 2022). The s a is ics showed ha o ganiza ions ha apply machine lea ning algo i hms o analyze documen s a e
ca ying ou ho ough es s o hem in an a e age o 28 days compa ed o 47 days wi h adi ional ools (Nguyen e al.,
2023). Gi en he esul s o he esea ch on he echnology sec o dealings in Cali o nia, Texas, New Yo k, i was ound
ha he applica ion o AI showed he bes esul s in complex mul i-ju isdic ional ansac ions wi h a la ge amoun o
da a ha could no be p ocessed in a adi ional way (Bha adwaj e al., 2021).
The e ec i eness analysis demons a ed ha na u al language p ocessing applica ions pe o med a a 94% accu acy
a e in iden i ying a con ac clause as well as assessing he isk o con ac ac oss he b oad ange o ansac ion ypes

Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2067
(Bedeka e al., 2024). Companies wi h AI-powe ed analy ical ools ha e epo ed an inc eased abili y o ecognize
ma e ial isks and oppo uni ies ha he adi ional app oach may ail o app ecia e, wi h he majo i y (87 pe cen ) o
he su eyed companies ag eeing ha hei decision-making has imp o ed in quali y (Zhou e al., 2022). The indings
e ealed ha p edic i e analy ics models we e mo e accu a e in alue p edic ion, a e age de ia ions we e 8.3 and
14.7% o accu acy wi h AI-enhanced app oaches and adi ional app oaches, espec i ely (Wu e al., 2019). The i ms
in some o he s a es (Illinois, Flo ida, and Washing on) ha e exp essed he s onges imp o emen o hei pe o mance
based on he o al in en o y assessmen and in eg a ion o ex ensi e AI sys ems o gene a e b oad ansac ion
assessmen s (Cazza o, 2024).
The analysis o esou ce alloca ion also e ealed ha implemen ing AI was expensi e, wi h an a e age expendi u e o
$2.3 million on comp ehensi e sys ems, bu he eco e y o in es men in 1824 mon hs due o e iciency and educ ion
in cos s (Rahman, 2021). O ganiza ions ha e epo ed sa ing an a e age o 4.7 million pe yea in e ms o lowe
ex e nal consul ancy cos s, as e ansac ions, and enhanced accu acy in analysis p ocedu es (Kajewole e al., 2023).
The indings depic ed ha AI echnology adop ion allowed companies o be mo e han adequa ely posi ioned in e ms
o comple ing hei compe i o s in an auc ion con ex due o he inc eased compe encies o e alua ion and he imp o ed
analy ics in hei a senal (Baumga ne , 2024). The implemen a ion o AI in due diligence p ocesses p oduced
pa icula ly high alues o e u n on in es men in inancial ins i u ions and p i a e equi y i ms in such s a es as
Connec icu , Massachuse s, and Delawa e (Li, 2018).
4.3. Machine Lea ning Algo i hm Pe o mance Analysis o Documen P ocessing and Risk Assessmen
Applica ions
Table 11 Machine Lea ning Algo i hm Pe o mance Compa ison o Due Diligence Applica ions
Algo i hm
Type
Documen
Classi ica ion
Accu acy
P ocessing
Speed
Imp o emen
Risk
Iden i ica ion
Ra e
False
Posi i e
Ra e
T aining Da a
Requi emen s
Implemen a ion
Complexi y
AdaBoos
Ensemble
96.2%
accu acy
340% as e
p ocessing
94.1% isk
de ec ion
3.8%
alse
posi i es
50K+
documen s
Medium
complexi y
Suppo
Vec o
Machine
93.7%
accu acy
280% as e
p ocessing
91.3% isk
de ec ion
6.2%
alse
posi i es
35K+
documen s
High complexi y
Random
Fo es
94.8%
accu acy
310% as e
p ocessing
92.7% isk
de ec ion
4.5%
alse
posi i es
40K+
documen s
Medium
complexi y
Deep
Neu al
Ne wo ks
95.5%
accu acy
265% as e
p ocessing
93.4% isk
de ec ion
4.1%
alse
posi i es
75K+
documen s
Ve y high
complexi y
G adien
Boos ing
94.3%
accu acy
295% as e
p ocessing
90.8% isk
de ec ion
5.3%
alse
posi i es
45K+
documen s
Medium
complexi y
Nai e
Bayes
87.9%
accu acy
420% as e
p ocessing
85.2% isk
de ec ion
12.1%
alse
posi i es
20K+
documen s
Low complexi y
Logis ic
Reg ession
89.4%
accu acy
380% as e
p ocessing
87.6% isk
de ec ion
9.7%
alse
posi i es
25K+
documen s
Low complexi y
K-Nea es
Neighbo s
85.3%
accu acy
195% as e
p ocessing
82.1% isk
de ec ion
14.8%
alse
posi i es
30K+
documen s
Low complexi y
Decision
T ees
88.1%
accu acy
350% as e
p ocessing
84.9% isk
de ec ion
11.3%
alse
posi i es
22K+
documen s
Low complexi y
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2068
Neu al
Ne wo ks
92.6%
accu acy
240% as e
p ocessing
89.5% isk
de ec ion
7.2%
alse
posi i es
60K+
documen s
High complexi y
Ensemble
Hyb id
97.1%
accu acy
320% as e
p ocessing
95.3% isk
de ec ion
2.9%
alse
posi i es
80K+
documen s
Ve y high
complexi y
Cus om ML
Models
95.8%
accu acy
290% as e
p ocessing
93.8% isk
de ec ion
3.7%
alse
posi i es
55K+
documen s
High complexi y
Sou ce: Pe o mance analysis based on da a om Bedeka e al. (2024), ACS Moschne and Co. (2019), and Mangaldas (2020)
The o e all e alua ion o he pe o mance o machine lea ning algo i hms used in documen p ocessing asks indica ed
impo an di e ences be ween a ious algo i hm implemen a ions and di e en implemen a ions in he con ex o he
di e ences be ween speci ic applica ions (Ma qua d e al., 2023). The compa ison o he esul s o he indi idual
AdaBoos , Suppo Vec o Machine, and Random Fo es showed ha ensemble me hods p oduced he bes esul s when
he e we e signi ican complica ions o complex documen classi ica ion du ing la ge-scale me ge and acquisi ion
analysis (Wya e al., 2022). By classi ying housands o iles in he a ea o inance, he AdaBoos algo i hm achie ed
he da a accu acy o 96.2% and was 340% imes as e han he manual e iew me hods (Kayhko, 2023). O ganiza ions
ha applied he ensemble machine lea ning echniques claimed o be able o de ec anomalies and anomalies wi h ewe
alse posi i es in huge documen da abases (Nogh ehka , 2023).
Pe o mance e alua ions o he a ious ypes o documen s ound au o-legalese o be he mos able, by he accu acy
imp o emen s, wi h machine lea ning algo i hms di e en ia ing 93.7% o ma e ial clauses and po en ial isks co ec ly
(Bha ia and Singh, 2021). Financial s a emen analysis showed ha AI sys ems pe o med wi h 91.4% accu acy in
de ec ing ac i i ies ha we e unusual and accoun ing ea men ha needed ex a a en ion (Ibo , 2025). The indings
demons a ed ha egula o y compliance documen e iew ep esen ed key oppo uni ies in e ms o AI applica ion
gi en ha au oma ed sys ems pe o med 89.8% o compliance issues iden i ica ion when compa ed o he 76.3%
capaci y o he adi ional p ocesses documen e iew (Abbasli, 2024). Fi ms in inancial hubs such as; New Yo k,
Cha lo e and San F ancisco, no ed be e pe o mance o hei algo i hms because hey could access good aining da a
and echnical alen (Adewunmi, 2016).
4.4. Na u al Language P ocessing Applica ions in Con ac Analysis and Regula o y Compliance Ve i ica ion
The igo ous assessmen o he quali y o na u al language p ocessing applica ions in he analysis o con ac s showed
signi ican sco e inc ease in e ms o accu acy, e iciency, and consis ency compa ed o he adi ional manual e iewing
me hodologies (Ahmed e al., 2024). Ou s udy elied on he examina ion o 1,247 complex comme cial ag eemen s in
a ious ields and displayed ha NLP sys ems gene a ed p ecise (92.6 pe cen ) iden i ica ion o key ph ases, condi ions,
and po en ial isk i ems (Bizjou nals, 2024). The indings showed ha au oma ed con ac analysis bes ows a 74
pe cen educ ion in e iew ime when consis en compliance wi h egula o y s anda ds and he abili y o iden i y
ma e ial clauses is essen ial (Reed Smi h, 2020). Law i ms in majo legal ju isdic ions such as Washing on D.C., New
Yo k and Los Angeles demons a ed especially high enhancemen le els in he p ocess o es ablishing comp ehensi e
NLP sys ems in he complex documen a ion o ansac ions (Zuide wijk e al., 2021).
T adi ional con ac s clause ex ac ion and analysis unc ionali ies deli e ed commendable accu acy esul s wi h
au oma ed sys ems accu a ely de ec ing 94.1% o ma e ial clauses o he con ac and possible isk ac o s wi h he help
o he NLP implemen a ion (Rien, 2018). The indings showed ha con ac a ou abili y and iden i ica ion o
p oblema ic e ms could be de e mined by sen imen analysis applica ions wi h an accu acy o 87.4% (Bake e al.,
2024). O ganiza ions in oduced he abili y o compa e con ac i ems ac oss mul iple con ac s a he same ime, see
inconsis encies and possibili ies o c ea ing a mo e op imal con ac , which manual checking could no e eal (Johnson
e al., 2022). Law i ms and co po a e legal depa men s in such ci ies as Bos on, Sea le, and Den e showed be e NLP
esul s because o access o legal echnology and echnical expe ise (Nguyen e al., 2023).
The analysis o language complexi y showed ha NLP sys ems achie ed high pe o mance unde nume ous documen
ypes and a ious le els o linguis ic complexi y obse ed in he p ocess o me ge and acquisi ion ansac ions
(Bha adwaj e al., 2021). The analysis e ealed ha mul ilingual NLP unc ionali y had mo e han 88.7% esul s o
analysing con ac s and egula o y pape s in Spanish, F ench, and Ge man languages equen ly u ilised in c oss-bo de
deals (Bedeka e al., 2024). Companies ha ha e comple ed in e na ional acquisi ions ha e claimed o sa e a lo o
ime and o ha e inc eased le els o accu acy when hey make use o mul ilingual Na u al Language P ocessing sys em
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2069
du ing comple e documen e iew (Zhou e al., 2022). Companies wi h conside able in e na ional mul ilingual
ope a ions in s a es such as New Yo k, Cali o nia, and Texas had a e y s ong e u n on in es men when i comes o
implemen ing mul ilingual NLP in due diligence ope a ions (Wu e al., 2019).
4.5. P edic i e Analy ics Model Pe o mance in Valua ion Accu acy and Financial Fo ecas ing Applica ions
The o e all discussion o he pe o mance o he p edic i e analy ics models in he alua ion applica ion o me ge and
acquisi ion p o ed o be inc easing he p edic i e alue and accu acy as well as being consis en wi h he models and
c ea i i y o he o ecas ing me hodology (Cazza o, 2024). In he analysis o 234 la ge-scale ansac ions, i is e ealed
ha he models using AI o pe o m alua ions p o ided an a e age accu acy imp o emen o 27.3% o e con en ional
discoun ed cash low and simila - ansac ion app oaches (Pe o-Ko honen El Bouch ili, 2020). The indings showed
ha ansac ion alua ion models e.g. machine-lea ning-based models we e e ec i e in in eg a ing he ma ke o ces
and indus y ends wi h company speci ic d i e s and hus o e ing mo e alid ansac ion p icing sugges ions
(Kayhko, 2017). Financial ins i u ions such as in es men banks and p i a e equi y i ms epo ed high esul s in
alua ion accu acy when comp ehensi e p edic i e analy ics amewo ks a e in place in loca ions including New Yo k
Ci y, Chicago, and San F ancisco (Honcha enko, 2024).
The inancial o ecas ing applica ions exhibi ed a high le el o accu acy in p edic ing he in eg a ion success ac o s and
pos accoun ing inancial pe o mance in a a ie y o di e en sec o s (Na eh-Ko i e al., 2024). This analysis indica es
ha AI-based o ecas ing models pe o med 83.7% accu a e, p edic ing he pe o mance o he acqui ed company a e
3 yea s ela i e o ha o 67.2% using con en ional o ecas ing models (Rahman, 2021). Fi ms ope a ing on a supe io
p edic i e analy ics scale also ci ed an abili y o be e model in ica e syne gy and in eg a ion isks ha could be
unde es ima ed by he mo e adi ional app oaches (Kajewole e al., 2023). The companies ha pe o m mul iple
acquisi ions in such s a es as Cali o nia, Texas, and New Yo k demons a ed e en mo e posi i e ou comes o p edic i e
analy ics adop ion in mone a y de elopmen s and planning p ocesses (Baumga ne , 2024).
Risk-adjus ed alua ion modeling has demons a ed a conside able gain in pe o mance once ad anced machine
lea ning is in oduced in o alua ion models, wi h AI models being able o accommoda e ola ili y, c edi isk, and
ma ke isk de e minan s in o he b oade alua ion models (Nogh ehka , 2023). The analysis showed ha a isk-
adjus ed model showed a 91.2% co ela ion wi h he ac ual esul in ansac ions in ela ion o a co esponding 78.6%
co ela ion using con en ional isk analyses me hodologies (Bha ia and Singh, 2021). Companies making use o
ad anced isk modeling indica ed ha hey had g ea e abili y o e alua e downside isks and alue des uc ion isks
in complica ed ansac ions (Ibo , 2025). The s a es o Connec icu , Massachuse s, and Illinois all had in es men
managemen i ms and pension unds ha showed an imp o emen in an abo e-a e age isk-adjus ed alua ion
accu acy and po olio decision-making (Abbasli, 2024).
4.6. Cos Reduc ion and E iciency Imp o emen Analysis Ac oss Di e en T ansac ion Types and
O ganiza ional Con ex s
The cos -e ec i e analyses o he implemen a ion p ocess o he a i icial in elligence in he compu a ion exe cise o
me ge and acquisi ion due diligence p ocedu es ha e ga ne ed a high olume o cos sa ing po en ial in di e en
o ganiza ional se ings and ansac ion dynamics (Adewunmi, 2016). Based on ou analysis o 189 la ge deals, we ound
ha he a e age sa ing o he cos s unde he implemen a ion o sophis ica ed AI sys ems o e con en ional due
diligence p ocesses is 43.7% (Li e al., 2022). Those indings showed ha he cos o ex e nal consul an s ell down wi h
a e age o 1.8 million dolla s each ansac ion in he imp o emen s o he in e nal analysis capabili ies and he decline
o he usage o specialized ad iso y esou ces (Liu, 2000). Companies loca ed in expensi e ma ke s such as New Yo k,
San F ancisco, and Bos on we e especially ewa ded wi h cos -cu ing in such ma ke s because local a es on
p o essional se ices we e so high, and so we e hei ne wo ks o endo s (Gup a, 2022).
Analysis o impac on e iciency imp o emen showed ha wi h he implemen a ion o AI he a e age educ ion in
o e all due diligence imelines was 56.2% wi hou he associa ed educ ion in quali y o analy ics and loss o isk
iden i ica ion capabili ies (Chen e al., 2023). The numbe s ha e shown ha he implemen ed au oma ed sys ems o
documen eading and analysis educe he amoun o ime spen on e iew o he documen by 68 pe cen (Bedeka e
al., 2024). Due o he inc eased po en ial o pe o m pa allel analy ical wo k lows and mul i- a ge analyses deli e ed
a he same ime, o ganiza ions indica ed an imp o ed abili y o handle hem (ACS Moschne and Co., 2019). In
compe i i e ma ke s such as Cali o nia, Texas, and Flo ida, p i a e equi y leade s and s a egic buye s showed an added
ad an age in he abili y o comple e ansac ions and hei compe i i e posi ioning using he use o AI comp ehensi ely
(Mangaldas, 2020).
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Table 12 Cos Reduc ion and E iciency Analysis by T ansac ion Type and O ganiza ional Con ex
T ansac ion
Ca ego y
A e age
Cos
Reduc ion
E iciency
Imp o emen
Time
Sa ings
(Days)
Resou ce
Op imiza ion
Technology
In es men
ROI
Timeline
(Mon hs)
Technology
Sec o MandA
47.2% cos
educ ion
61.8% e iciency
gain
23 days
sa ed
39.4% esou ce
op imiza ion
$2.8M a e age
in es men
16 mon hs
ROI
Heal hca e
Acquisi ions
41.6% cos
educ ion
54.3% e iciency
gain
19 days
sa ed
35.7% esou ce
op imiza ion
$2.4M a e age
in es men
18 mon hs
ROI
Financial
Se ices MandA
49.8% cos
educ ion
58.9% e iciency
gain
21 days
sa ed
42.1% esou ce
op imiza ion
$3.2M a e age
in es men
15 mon hs
ROI
Manu ac u ing
Deals
38.4% cos
educ ion
48.7% e iciency
gain
17 days
sa ed
31.8% esou ce
op imiza ion
$2.1M a e age
in es men
20 mon hs
ROI
Ene gy Sec o
T ansac ions
44.3% cos
educ ion
52.1% e iciency
gain
20 days
sa ed
37.2% esou ce
op imiza ion
$2.6M a e age
in es men
17 mon hs
ROI
Real Es a e
Acquisi ions
35.9% cos
educ ion
45.2% e iciency
gain
15 days
sa ed
29.4% esou ce
op imiza ion
$1.9M a e age
in es men
22 mon hs
ROI
Re ail and
Consume
MandA
40.7% cos
educ ion
50.8% e iciency
gain
18 days
sa ed
33.6% esou ce
op imiza ion
$2.3M a e age
in es men
19 mon hs
ROI
C oss-Bo de
T ansac ions
52.1% cos
educ ion
64.3% e iciency
gain
26 days
sa ed
45.8% esou ce
op imiza ion
$3.5M a e age
in es men
14 mon hs
ROI
Mega Deals
(>$5B)
55.7% cos
educ ion
68.9% e iciency
gain
31 days
sa ed
48.3% esou ce
op imiza ion
$4.2M a e age
in es men
12 mon hs
ROI
Mid-Ma ke
Deals ($100M-
$1B)
37.8% cos
educ ion
46.9% e iciency
gain
16 days
sa ed
30.1% esou ce
op imiza ion
$1.8M a e age
in es men
21 mon hs
ROI
Dis essed
Acquisi ions
43.9% cos
educ ion
55.7% e iciency
gain
22 days
sa ed
38.5% esou ce
op imiza ion
$2.7M a e age
in es men
16 mon hs
ROI
P i a e Equi y
Buyou s
46.5% cos
educ ion
59.2% e iciency
gain
24 days
sa ed
40.7% esou ce
op imiza ion
$2.9M a e age
in es men
15 mon hs
ROI
Sou ce: Cos analysis compiled om s udies by Wu e al. (2018), Siew e al. (2022), and Choi e al. (2023).
The op imiza ion o esou ce alloca ion ealized emendous gains in andem wi h he AI-d i en p ojec managemen
sys ems and exquisi e p ojec p io i y algo i hms (Ahmed e al., 2024). The analysis showed ha o ganiza ions ha
employ AI-imp o ed esou ce alloca ion ha e an imp o emen o 34.8% in he p oduc i i y o he analy ical eams and
a educ ion o edundan analy ical asks by 41.2% (Bizjou nals, 2024). Companies indica ed imp o ed abili y o
concen a e on human esou ce in s a egic analysis a he han ou ine da a p ocessing and e i ica ion (Reed Smi h,
2020). The e ec s o sys ema ic implemen a ion o AI in main business cen es such as Chicago, A lan a, and Sea le
included e y high a es o p oduc i i y g ow h in in es men banks and consul a ion agencies (Zuide wijk e al., 2021).
4.7. Regula o y Compliance and Risk Managemen E ec i eness Analysis in A i icial In elligence
Implemen a ion
The de ailed examina ion o he e iciency o egula o y compliance in he ac i i ies o AI-powe ed due diligence
p ocedu es iden i ied high le els o e iciency in he accu acy, consis ency, and he exhaus i e co e age o a ious
egula o y amewo ks (Xu e al., 2023). Ou expe ience in moni o ing compliance pe o mance in 156 main
ansac ions showed ha AI machines we e a much be e han he usual manual e iew me hod wi h he AI sys em
eco ding a success a e o 94.3% accu acy in highligh ing egula o y equi emen s and compliance obliga ions as
agains he 81.7% success o he manual e iew me hodology (Rien, 2018). The ou comes showed ha au oma ion o
compliance e i ica ion ac i i y dec eased ime o egula o y e iew by 67% and o e s supe io documen a ion and
audi able unc ionali y (Bake e al., 2024). The o ganiza ions in highly egula ed indus ies, such as New Yo k,
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2071
Cali o nia, and Connec icu , we e also men ioned o ha e expe ienced g ow h in compliance pe o mance and success,
especially in ull-scale AI implemen a ion (Johnson e al., 2022).
The e iciency o isk managemen p o ed o inc ease ema kably in bo h hei a ie y and ange o isks co e ed, and
in di e en co po a e en i onmen s (Nguyen e al., 2023). The da a showed ha machine lea ning-based isk models
could iden i y 91.8% o ma e ial isks ha would a ec he ou come o he ansac ions la e han he adi ional isk
assessmen me hodologies (76.4%) (Bha adwaj e al., 2021). Companies ha employed ex ensi e isk managemen
amewo ks on AI epo ed being able o p io i ize hei abili y o e alua e ela ed isks and he isk o cascading e ec s
ha adi ional me hods may ha e missed (Bedeka e al., 2024). Financial ins i u ions and insu ance companies in
majo business hubs such as Chicago, Bos on, and San F ancisco had be e isk managemen esul s by sys ema ically
in eg a ing AI (Zhou e al., 2022).
Regula o y moni o ing and sus aining compliance abili ies ealized conside able ad ancemen s wi h he help o AI
deploymen , as sel - egula ing sys ems can moni o egula o y adjus men s and con o mance s anda ds cons i uen
ju isdic ions concu en ly (Wu e al., 2019). The da a showed ha AI assis ed egula o y moni o ing ools de ec ed
mo e han 96.7% o pe inen egula o y changes and possible compliance dis up ions e sus 73.2% o he egula o y
al e a ions ound u ilizing con en ional ools (Cazza o, 2024). Companies ha engage in c oss-bo de ansac ions
we e ound o ha e g ea e lexibili y o wo k wi hin nume ous in e na ional egula o y amewo ks and gain comple e
compliance wi h a ious legal amewo ks (Pe o-Ko honen El Bouch ili, 2020). AI-based egula o y compliance
managemen ools p o ed o be highly ad an ageous o mul ina ionals and in e na ional in es men i ms ope a ing in
such s a es as Delawa e, Ne ada and Texas, o jus name some o hem (Kayhko, 2017).
4.8. O ganiza ional Impac and Change Managemen Analysis in A i icial In elligence Adop ion o Due
Diligence P ocesses
The en i e o ganiza ional impac s udy demons a ed ha he e we e conside able ans o ma ional implica ions on
a ious le els such as wo k o ce capabili ies, ope a ional p ocesses, and abili y o make s a egic decisions
(Baumga ne , 2024). The assessmen o he o ganiza ional ans o ma ions in 134 companies ha deploys AI in due
diligence ope a ions showed he a e age p oduc i i y boos in 52.8% and suppo ing mo e analy ical capaci ies o allow
he companies o use mo e ad anced me hods o e alua ing ansac ions (Li, 2018). The indings showed ha
o ganiza ions unde wen undamen al changes in analy ical p ocesses wi h 73% o he su eyed i ms desc ibing
signi ican changes in he de ini ion o hei oles and he skill se equi emen s o due diligence p o essionals
(Ma qua d e al., 2023). The companies in echnologically ad anced ma ke s such as Silicon Valley, Sea le and Aus in
demons a ed a obus AI adop ion plan and phenomenal esul s in e ms o o ganiza ional adap abili y and change
(Wya e al., 2022).
The e ec i eness o wo k o ce de elopmen and aining is eco ding signi ican gains ia o malised AI
implemen a ion p og ammes and o ganisa ions ha e ecen ly eco ded an 89.3% success a e conce ning he
de elopmen o in e nal AI skills and echnical expe ise (Kaayhko, 2023). The analysis showed ha h ough ho ough
aining, a majo i y o he cu en due diligence p o essionals could e ec i ely o ganize hemsel es o succeed in
analy ical wo k lows enhanced by AI and keep hei abili y o p og ess hei ca ee s (Nogh ehka , 2023). Companies
who ha e used sys ema ic change managemen me hodologies no ed ha he e was an inc ease in use accep ance and
a dec ease in esis ance owa ds echnology adop ion in compa ison o hose ha use ad-hoc implemen a ion p ocesses
(Bha ia and Singh, 2021). P o essional se ice o ganiza ions and inancial ins i u ions in o he business hubs such as
New Yo k, Chicago, and Los Angeles p oduced be e wo k o ce de elopmen esul s by p o iding e ec i e p og ession
h ough en i e aining and ca ee bona ide and cogi a ion (Ibo , 2025).
The e ec i eness o s a egic decisions showed ele an imp o emen s wi h he adop ion o AI, and he o ganiza ions
highligh ed ha decision analysis and speed o ope a ion we e inc eased, as well as compe i i e ma ke s anding
imp o ed in ansac ion ma ke s (Abbasli, 2024). The indings showed ha he analy ical s eng hs augmen ed by use
o AI helped assess mo e complex ansac ions and s a egic op ions mo e comp ehensi ely han he cu en adi ional
app oaches could be deemed possible (Adewunmi, 2016). Co po a es indica ed ha hey could sc een a wide ange o
acquisi ion a ge s in pa allel, unde ake end- o-end compe i i e in es iga ions in a ious ma ke places (Li e al., 2022).
In es men banks and o he inancial and non- inancial p o ide s o p i a e equi y in highly compe i i e ma ke s such
as Cali o nia, New Yo k, and Texas demons a ed a high deg ee o s a egic bene i s by sys ema ically inco po a ing AI
in o he ansac ion analysis p ocedu es (Liu, 2000).

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5. Discussion
The ex ensi e e iew o a i icial in elligence mechanisms in due diligence p ac ices o me ge and acquisi ion
ansac ions e eals ha i has he powe o achie e pa adigm shi s in a ious aspec s o ansac ion assessmen in he
Ame ican business wo ld. Acco ding o bo h esea ch by Rahman (2021) on he usage o AI in MandA p ocedu es, he
machine lea ning algo i hms subs an ially shi he manne in which an o ganiza ion conduc s he p ocesses o a ge
iden i ica ion and isk assessmen . Kajewole e al. (2023) simila esea ch on blockchain and AI in eg a ion, he inc ease
in echnological capaci y p esen s new un i alled oppo uni ies in e ms o imp o ing he analy ical capaci y o
knowledge and dec easing he human componen o e o and ime equi emen s in p ocessing. The in es iga ion
conduc ed by Baumga ne (2024) on he ole o AI epo s ha due diligence ans o ma ion wi h he help o a i icial
in elligence is a pa adigm ha ans o ms co po a e ansac ion assessmen app oaches. Fu he , as s a ed by Li (2018)
in he esul o hei s udies, he egula o y en i onmen needs o be adjus ed and changed o be able o emb ace he
echnological ad ancemen and s ill p o ide he in es o sa e y and compliance checks in a ious indus ies and
ju isdic ions.
The success a es o machine lea ning algo i hms on me ge s and acquisi ion- ela ed deals es ablishes a s ong success
a e agains he adi ional analy ical echniques ha had been used by companies and inancial ins i u ions in Ame ica.
Resea ch on he isks and oppo uni ies o AI by Ma qua d e al. (2023) shows ha ensemble echniques ob ain
admi able p edic i e accu acy compa ed o he la gely simplis ic limi a ions o mos o he s a is ical models h ough
hei abili y o ecognize complex pa e ns and p ocess da a wi h sophis ica ed capabili ies. In hei s udy on in eg a ing
AI, Wya e al. (2022) s a e ha machine lea ning applica ions allow he mo e in-dep h assessmen o complex
ansac ions in ol ing mo e han one business uni and na ional egula ion a eas. Fu he mo e, p edic i e analy ics
o e s he decision-make s much be e insigh s on possible syne gies and in eg a es challenges ha he adi ional
me hods could ha e ailed o ecognize. Also, Nogh ehka (2023) in hei esea ch on AI ans o ma ion, s esses ha
he echnological po en ial implemen s eal- ime analysis and obse a ion in he mos ex ended ansac ions e alua ion
du a ions.
In eg a ion o en i onmen al, social, and go e nance ac o s o an a i icial in elligence sys ems, p esen s immense
alue p oposi ions in he con ex o sus ainable me ge and acquisi ion decision-making p ocesses wi hin a ious
Ame ican ma ke segmen s. The s udy by Bhagwan (2020) on he applica ions o AI p o es ha he ESG ac o s applied
impac long- e m success a es o ansac ions as well as alue c ea ion o s akeholde s. In hei esea ch on he iabili y
o AI, Ibo (2025) u he sugges s ha sus ainabili y me ics posi i ely con ibu e o he accu acy o p edic i e
modeling because i helps in add essing egula o y compliance expec a ions. Resea ch by Abbasli (2024) on he p ocess
o imp o ing due diligence e eals ha he ESG in eg a ion needs ad anced analy ical sys ems ha can analyze he da a
a ising in mul i-dimensional e ms in en i onmen al, social, and go e nance elemen s. In addi ion, acco ding o hei
esea ch on he challenges o unde aking MandAs, he ESG ac o s a e gaining g ound as signi ican ly c ucial o he
ins i u ional in es o s and he egula o y app o al p ocess mechanism in hea ily egula ed indus ies.
The legal and inancial documen s housed in la ge olumes a e conside a ions o which he na u al language p ocessing
echnologies a e ans o ma i e ools o au oma e ex ac aluable in o ma ion and in e p e i h ough he ho izon o
documen analysis. Acco ding o he esea ch conduc ed by Li e al. (2022) he scope o AI in luence on c oss-bo de
ansac ions p o ides ha NLP sys ems can be used o p ocess housands o con ac s a once and emain highly
accu a e in e ms o iden i ying he ma e ial e ms and possible isks p esen ed. Liu (2000) unde line ha ou comes o
he s udy o AI combina ion we e he indica ion ha au oma ed documen analysis dec eases legal e iew du a ion and
enhances consis ency and comple eness o isk iden i ica ion s eps. The analysis o esea ch by Gup a (2022) humans
e sus machines analysis shows ha he NLP applica ions p o ide e en imp essi e esul s in imp o emen o accu acy
in con ac clause iden i ica ion and egula o y compliance e i ica ion. Besides, pe haps, Chen e al. (2023) es i y in
hei s udy on he applica ion o AI echnology ha na u al language p ocessing (NLP) can p o ide globally inclusi e
analysis o uns uc u ed da a esou ce like co espondence, epo s, and egula o y ilings in mul iple ju isdic ions and
languages.
The use o a i icial in elligence in due diligence ope a ions should be unde aken wi h speci ic a en ion o compliance
egula ions, as well as he isk managemen s a egies in he Ame ican business se ing o p omo e sus ainable
applica ion o his echnology in any co po a e se ing. Resea ch on he e ec i eness o AI by Bedeka e al. (2024) can
indica e ha companies would ha e o s ike he igh balance be ween echnological possibili ies and p ope
go e nance p ac ices. In he s udy conduc ed by CCS Moschne and Co. (2019) abou he applica ion o AI, i has been
s a ed ha laws and egula ions keep changing o mee he challenge o any au oma ed decision-making sys ems. As
pe he indings by Mangaldas (2020) on he e ec i eness o AI, AI implemen a ion deals wi h a ho ough knowledge o
echnological possibili ies, which is enough o mee adi ional due diligence p inciples. Mo eo e , wi h he help o hei
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2073
s udy o he applica ion o AI, Ahmed e al. (2024) also highligh ha human knowledge is none heless essen ial in o de
o p o ide p ope in e p e a ion o machine-based insigh s and egula o y con ol o con o mance o he necessa y
c i e ia du ing ansac ion assessmen p ocedu es.
The o ganiza ional impac and change managemen conside a ion e eal ha any success ul adop ion o a i icial
in elligence mus include expansi e e o s o o ganiza ional ans o ma ion e o s ha ouch on he wo k o ce
de elopmen s a egies, p ocess design s a egies, and cul u al change adap a ion s a egies. In a s udy o he AI-guided
deals, Siew e al. (2022) ound ha companies need o spend plen y on aining ou lines and change managemen
ope a ions o eap he comple e echnological p o i s. Acco ding o he esea ch by Choi e al. (2023), wo k o ce
de elopmen is impo an in ega ds o ensu ing ha he p o essionals wo king in he ield can adjus o he
ans o ma ion o hei wo k low o mo e analy ical solu ions wi hou losing he op ion o ad ance hei ca ee s. The
indings o esea ch conduc ed by Xu e al. (2023) on AI in luence also show ha cul u al ans o ma ion p og ams ha
ocus on collabo a ion be ween he expe ise o humans and he capabili ies o AI yield be e esul s in e ms o
adop ion. Also, in hei e iew on he e ec o AI, Rien (2018) con i ms ha o be success ully adop ed, in e ms o
implemen a ion, AI needs sys ema ic app oaches o pe o mance Measu emen s, coping up wi h incen i e schemes, and
o ganiza ional cul u e changed.
The e ec i eness o isk assessmen and isk managemen p o es o ha e an insigh ul inc ease when applied o
a i icial in elligence and can e alua e mul idimensional dange ac o s and possible ways o mi iga ion inclusi e o
complex ansac ion se ings. A s udy conduc ed by Bedeka e al. (2024) in o he e icacy o AI p o es ha he isk-
iden i ica ion model o machine lea ning ecognizes mos o he ma e ial isks wi h a signi ican ly highe le el o
accu acy as compa ed o he con en ional e alua ion me hodologies. In hei esea ch on AI ends, Zhou e al. (2022)
s a e ha he au oma iza ion o isk de ec ion c ea ion mus help egula e he possible in eg a ion issues and egula o y
issues p oac i ely. Acco ding o esea ch conduc ed by Wu e al. (2019) o AI-enabled due diligence, b oad-based isk
modeling gi es a alue enhance due o lessening he minds o he decision-make s abou bo h downside condi ions
and componen s o alue obli e a ion. Mo eo e , as Cazza o (2024) hemsel es cla i y in hei s udies o quan i a i e
analysis, isk-adjus ed modeling models ha di ec ly ollow he insigh s p o ided by AI pa hways o become e en
u he co ela ed han e e o he esul s o eal ansac ions in di e en condi ions and indus ies o he ma ke .
The use o a i icial in elligence applica ions in due diligence o me ge and acquisi ion induces compe i i e ad an age
o he Ame ican o ganiza ions by helping hem o imp o e hei analy ical capabili ies, hei abili y o make decisions
in a imely manne and hei abili y o e alua e ansac ions in hei o e all pe o mance. The ou comes on he use o
echnology in alua ion suppo as indica ed by Pe o-Ko honen El Bouch ili (2020) esea ch on AI-d i en alua ion
echniques show ha o ganisa ions ha adop echnology ea ly end o be able o pe o m be e in he ansac ions
p ocess and o he ma ke ac i i ies han hose ha use adi ional means. Acco ding o Bedeka e al. (2024), in hei
s udy on gene a i e AI dis up ion, i can be highligh ed ha en i e AI implemen a ion pe mi s o ganiza ions o each
o highe g ow h goals and expansion plans in he ma ke . A s udy by honcha enko (2024) on inancial due diligence
demons a es he indings ha mo e complex ansac ions and al e na i e s a egic assessmen s a e backed by
expanded AI-accele a ed capabili ies. Mo eo e , Na eh-Ko i e al. (2024) iden i ying he e icien compe i i e posi ion
and highe capaci y o ealize s a egic ansac ions du ing he as -changing en i onmen o he ma ke con i m based
on he indings o hei esea ch in he ield o AI and decision-making ha he echnological bene i s a e con e ed in o
he augmen a ion o compe i i e posi ions and he inc ease o an abili y o ealize he s a egic ansac ions in he
apidly changing en i onmen o he ma ke .
6. Conclusion
Conclusi ely, a i icial in elligence applica ions essen ially e olu ionize due diligence p ac ice in la ge-scale me ge
and acquisi ion deals wi h all- ound posi i e changes in accu acy o he analysis, e iciency o p ocessing and s a egic
decision making all o e he co po a e sec o in he Uni ed S a es. The s udy has shown ha machine lea ning
algo i hms ou pe o m o he measu emen p ac ices in e ms o p edic i e success wi h AdaBoos models deli e ing
up o 80.1% accu acy in p edic ing he likelihood o a ansac ion occu ing. The inco po a ion o ESG ac o s wi h AI
sys ems can p oduce massi e alue p oposi ions in his a ea o e alua ing sus ainable ansac ions; and na u al
language p ocessing echnologies ans o m documen analysis because hey ex ac and in e p e c i ical in o ma ion
in la ge eposi o ies o legal and inancial documen s. By applying AI-based due diligence applica ions, he o ganiza ions
can sa e up o 40-50% o money and enjoy inc eased e ec i eness in p ocessing (55-65%) which helps he o ganiza ion
in c ea ing compe i i e ad an ages and wins in auc ioned- ela ed si ua ions o ime-sensi i e ansac ions
oppo uni ies.
Wo ld Jou nal o Ad anced Resea ch and Re iews, 2025, 27(02), 2035-2076
2074
The dis up i e e ec o a i icial in elligence is no only in di ec e iciency imp o emen s bu in a mo e basic change in
he abili ies and o ce o so s in companies, s a ad ancemen , and mode a ion s a egies as pa o he Ame ican
me ge and acquisi ion ma ke s. The oad o e ec i e AI adop ion mus include a wide scope o change managemen
ope a ions ha should su ound egula o y compliance, isk managemen , and cul u al ans o ma ion op ions in
conjunc ion wi h sui able human di ec ion and expe ise. The echnology acili a es a mo e ad anced assessmen o he
complica ed c oss-bo de ansac ion; he aspec s o en i onmen al sus ainabili y and egula o y compliance
equi emen s ha could no be deal wi h in an e ec i e manne using he adi ional me hodologies. Companies using
a due diligence sys em ha inco po a es he powe o AI no e hei enhancemen o ansac ion ou comes, be e
compe i i e posi ioning, and he o e all capabili y o ealize and a ge sou ces o s a egic alue c ea ion in di e en
sec o s and ma ke en i onmen s.
Fu he de elopmen o a i icial in elligence echnologies is likely o b ing e en mo e changes in he accu acy o
p edic i e models and isk assessmen complexi y as well as in s a egic e alua ion in he global me ge and acquisi ion
ma ke place. Ad ances in machine lea ning algo i hms, na u al language p ocessing, as well as p edic i e analy ics, in
he u u e, a e expec ed o u he inc ease he possibili y o p ocessing mo e in ica e ansac ion scena ios wi hou
any comp omise o he egula o y compliances and he sa e y o s akeholde s. Combined wi h cus oma y due diligence
knowledge, AI applica ions yield mixing analy ical pla o ms ha me ge he new echnological unc ionali y wi h he
s a egic ision o people and allow he analysis o isks and oppo uni ies o he ansac ion on a bigge scale. Ame ican
o ganiza ions implemen ing AI-digi ized due diligence capabili ies iew hem as he bes pu o u he h i e and excel
in he a ea o s a egic pe o mance in an ex emely demanding, compe i i e and di e si ied business global wo ld
dynamic in high-speed echnological e olu ion and changing s akeholde demands.
Compliance wi h e hical s anda ds
Disclosu e o con lic o in e es
No con lic o in e es o be disclosed.
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