i
EVALUATING CREDIT DEFAULT RISK IN P2P
LENDING
Ri a Se as Celo ico Da Sil a Fialho
A Ma ke Ma u i y Pe spec i e
Disse a ion p esen ed as pa ial equi emen o ob aining
he Mas e ’s deg ee in S a is ics and In o ma ion
Managemen
ii
NOVA In o ma ion Managemen School
Ins i u o Supe io de Es a ís ica e Ges ão de In o mação
Uni e sidade No a de Lisboa
EVALUATING CREDIT DEFAULT RISK IN P2P LENDING
by
Ri a Se as Celo ico Da Sil a Fialho
Disse a ion p esen ed as pa ial equi emen o ob aining he Mas e ’s deg ee in S a is ics and
In o ma ion Managemen
Ad iso : P o . D . Jo ge Miguel Ven u a B a o, PhD
No embe 2021
iii
ABSTRACT
The Pee - o-Pee (P2P) lending indus y has g own signi ican ly in ecen yea s, owing i s ac ion o a
g owing in e es in he ma ke om bo h indi idual bo owe s and lende s alike. Such pla o ms ha e
g ea ly bene i ed om he ad en o digi al ans o ma ion and expanding in e ne oo p in ac oss
people o all ages and backg ounds. These pla o ms le e age no el ways o in e ac ing o acili a e an
al e na i e means o inancing which p o ides mo e oppo uni ies o bo owe s who may, o he wise
no ha e access o deb h ough con en ional mechanisms, and mo e in es men oppo uni ies o
lende s who may be seeking o di e si y hei po olios.
This s udy le e ages a da ase made a ailable by he Lending Club P2P lending pla o m, con aining
mo e han 2 million loan en ies, amassed o e mo e han 10 yea s. Being a pionee in he indus y,
Lending Club’s ack eco d and da a p o ide a good window in o he inne wo kings o such pla o ms
and hei abili y o assess he c edi wo hiness o bo owe s and loan applica ions.
We se ou o build upon p io wo k done in his space by unde s anding and analyzing his da ase o
iden i y he de e minan s o de aul o loans issued h ough P2P lending pla o ms. Ou analysis is also
employed o c ea e a p edic i e model which is hen es ed agains ou da ase . This app oach builds
upon p e ious s udies by ou lining an end- o-end p ocess o analyze and assess a pla o m’s abili y o
adequa ely p edic c edi de aul isk.
We ha e ound ha , in alignmen wi h p io wo k, such pla o ms a e indeed able o adequa ely assess
c edi de aul isk, in he way ha g ades a e assigned o indi idual loans. The logis ic eg ession model
which we ha e buil has also yielded good esul s in p edic ing de aul ed loans, while exhibi ing
medioc e pe o mance in classi ying ully epaid loans as likely cases o de aul .
KEYWORDS
C edi Risk; C edi De aul ; De aul Risk; P2P Lending; Lending Club; Logis ic Reg ession; Da a
Analysis; De e minan s o De aul
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INDEX
1 In oduc ion ................................................................................................................ 8
1.1 Lending Club ......................................................................................................... 9
2 Li e a u e e iew ....................................................................................................... 13
2.1 Online P2P lending his o y and cha ac e iza ion ............................................... 13
2.2 Risks o online P2P lending ma ke s – he good and he bad ............................ 13
2.3 De e minan s o de aul in online P2P lending ma ke s .................................... 15
2.4 Risk sco ing in online P2P lending ma ke s ........................................................ 15
3 Me hodology ............................................................................................................. 17
3.1 The Model - Logis ic Reg ession ......................................................................... 17
3.2 Pe o mance Measu emen s o he Model ....................................................... 18
4 Da a ........................................................................................................................... 20
4.1 Da a Desc ip ion ................................................................................................. 20
4.2 Da a Re inemen ................................................................................................ 21
4.3 Explo a o y Da a Analysis .................................................................................. 24
4.4 Final Va iables .................................................................................................... 28
5 Resul s and discussion ............................................................................................... 30
5.1 Model and Resul s .............................................................................................. 30
5.2 Model Pe o mance ........................................................................................... 33
6 Conclusions ............................................................................................................... 35
6.1 Limi a ions and Fu u e Wo k ............................................................................. 36
7 Bibliog aphy .............................................................................................................. 37
8 Appendix ................................................................................................................... 42
8.1 Appendix A ......................................................................................................... 42
8.2 Appendix B ......................................................................................................... 47
8.3 Appendix C ......................................................................................................... 48
LIST OF FIGURES
Figu e 1 – Lending Club’s Business Model ................................................................................ 9
Figu e 2 - Numbe o loans issued by yea .............................................................................. 20
Figu e 3 - Numbe o obse a ions by employmen leng h .................................................... 22
Figu e 4 - Dis ibu ion o annual income ................................................................................ 25
Figu e 5 - D i ou lie s .............................................................................................................. 25
Figu e 6 - Rela ionship be ween in e es a e and subg ade .................................................. 26
Figu e 7 - Co ela ion Ma ix o nume ical a iables .............................................................. 27
Figu e 8 - ROC Cu e ............................................................................................................... 34

i
LIST OF TABLES
Table 1 - Con usion Ma ix ...................................................................................................... 18
Table 2 - Summa y o he employmen leng h a iable .......................................................... 22
Table 3 - Numbe o loans by loan s a us ............................................................................... 23
Table 4 - Summa y o he annual income a iable ................................................................. 24
Table 5 - Dis ibu ion o loans by home owne ship ................................................................ 27
Table 6 - Logis ic Reg ession esul s ....................................................................................... 31
Table 7 - Con usion Ma ix ...................................................................................................... 33
Table 8 - Model Pe o mance Measu es ................................................................................ 33
Table 9 - All Lending Club a iables wi h desc ip ion ............................................................. 42
Table 10 - Co ela ion ma ix o LC a iables .......................................................................... 47
Table 11 - Desc ip i e S a is ics o Nume ical Va iables ......................................................... 48
Table 12 - Desc ip i e S a is ics o Ca ego ical Va iables ....................................................... 49
ii
LIST OF ABBREVIATIONS AND ACRONYMS
ANNs A i icial Neu al Ne wo ks
AUC A ea Unde he ROC Cu e
BME Bayesian Model Ensembles
DA Disc imina e Analysis
DT Decision T ees
DTI Deb o Income
FPR False Posi i e Ra e
GA Gene ic and E olu iona y Algo i hms
LR Logis ic Reg ession
MLM Maximum Likelihood Me hod
NN Neu al Ne wo ks
P2P Pee - o-pee
OR Odds Ra io
RF Random Fo es
ROC Recei e Ope a ing Cha ac e is ic cu e
SEC Secu i ies and Exchange Commission
SVM Suppo Vec o Machine
TPR T ue Posi i e Ra e
8
1 INTRODUCTION
In mode n his o y, lending is conside ed o be one o he key ac i i ies ca ied ou by inancial
ins i u ions. I is h ough he ins umen o c edi ha a ious economic ac i i ies see hei sp ead and
di e si ica ion, om p oduc ion o consump ion. I is h ough c edi ha businesses and indi iduals
uel hei needs o g ow and imp o e.
In he ac i i y o “lending” money, wo pa ies en e a ansac ion in which a lende “lends” money o
a bo owe , wi h a p omise o g adual epaymen . Rega dless o he e ms o epaymen , he e is
always a isk associa ed wi h he bo owe no being able o pay back he loan. This isk is classi ied as
“c edi isk” and is, hence, a measu e o he likelihood ha a bo owe may de aul on an issued loan,
hus ailing o mee he e ms de ined in he con ac en e ed by bo h pa ies.
Gi en he na u e and unce ain y o he c edi ma ke and conside ing he isks inhe en o he money-
lending ac i i y, inancial ins i u ions ha e long sough o bo h de elop and imp o e he mechanisms
h ough which hey e and check he quali y o bo owe s o whom hey lend money, as a means o
educing c edi isk and, ul ima ely, making back hei ini ial in es men .
The inancial c isis o 2007-2010 had a widesp ead and global impac on he en i e inancial indus y
and as ly a ec ed o he sec o s o he economy, leading o a educ ion in he possibili y o ecou se
o adi ional c edi by consume s and small businesses and dis us and dissa is ac ion wi h
comme cial banks and o he es ablished inancial ins i u ions.
The ad en o he in e ne and he inc ease in popula i y o web se ices and new business models
based on le e aging he bene i s o in e connec i i y and as , ubiqui ous access o he in e ne ,
coupled wi h he a o emen ioned dis us and discon en wi h adi ional inancial ins i u ions and
in e media ies, u he accele a ed he ise o al e na i e inance in he con ex o a globalized wo ld
economy.
Al e na i e inancial se ices compose an a ay o inancial se ices o e ed by p o ide s ha may
ope a e ou side o he umb ella o egula ed ins i u ions. Despi e many o he p oduc s and se ices
p o ided by hem no being “al e na i e”, bu a he he same as, o simila o hose o , adi ional
p o ide s, ins i u ions in his segmen a e o en cha ac e ized by ha ing mo e inhe en isk o hei
ope a ions and by execu ing adi ional unc ions/se ices ia al e na i e means o in e ac ion (e.g.
le e aging he In e ne o connec inancial pee s in a ansac ion) (B adley e al., 2009).
Pee - o-pee (P2P) lending is he p ac ice in which indi iduals lend money o o he indi iduals h ough,
gene ally, an online pla o m ha di ec ly connec s lende s wi h bo owe s wi hou he in ol emen
o a adi ional inancial in e media y. Whe e in con en ional lending a inancial ins i u ion, such as a
bank, would lend money o an indi idual, equen ly aking some ype o colla e al o secu e i (such
as a house o an au omobile), his new o m o lending complemen s he ma ke by opening lending
as an in es men ehicle di ec ly a ailable o indi iduals, and expanding he oppo uni ies p o ided o
a wide ange o bo owe s wi h, po en ially less es ic ions o cons ain s (such as age,
c edi wo hiness and employabili y).
P2P lending pla o ms o e simple egis a ion p ocesses o bo h bo owe s and lende s alike,
allowing hem o submi de ails o hei c edi needs and unding in e es , espec i ely. A e
9
conduc ing he necessa y due diligence, hey connec bo owe s and lende s o ill loan eques s and
p o ide unding ac oss a ange o isk le els (based on he c edi a ing p o ided o each loan).
The i s su acing o p ac ical, gene ally accep ed and unc ional pla o ms aces back o Zopa, a
B i ish P2P lending company, ounded in 2005 in he UK. Soon a e i , o he companies ollowed he
same model, wi h many non- iable candida es and al e na i es coming o be. O he success ul
pla o ms we e also s a ed in he US, wi h P ospe (2006) and Lending Club (2007) being he mos
no able men ions. They a e cu en ly he wo la ges P2P companies in he US, espec i ely holding
igu es o US$38 billion in issued loans and o e 2.5 million cus ome s o Lending Club and US$13
billion in issued loans and o e 820 housand cus ome s o P ospe (a he ime o w i ing).
Despi e he espec able igu es p esen ed abo e, and he g ow h end e idenced in he sec o (which
is expec ed o g ow o a US$897.85 billion ma ke by 2024), he P2P lending segmen s ill holds a
conside ably small pe cen age o all consume c edi issued, when compa ed o he global igu es o
US$3.9 illion. (T anspa ency Ma ke Resea ch, 2016)
P2P lending pla o ms do, none heless, me i he a en ion hey ecei e. They con inue he end o
disin e media ion ha online business models ha e es ablished in mode n-day In e ne and, hus,
p o ide di ec access o unsecu ed consume c edi as an al e na i e in es men ehicle o indi idual
lende s and do so a a much educed “in e media ion” cos due o de ia ing om he s anda d p ac ice
o adi ional c edi o ganiza ions. They also democ a ize access o consume c edi , by employing he
a o emen ioned model, and b ing i o he each o he o he wise non-c edi wo hy, by applying he
necessa y sa egua ds and adequa ely p icing isk. (He zens ein, And ews, Dholakia, & Lyand es, 2008)
1.1 LENDING CLUB
To allow o be e comp ehension o he da a which will be used in la e chap e s, i is impo an ha
we i s de elop an unde s anding o he subjec unde s udy (Lending Club), by e iewing how i
ope a es and wha ex e nali ies condi ion i s ope a ion.
Figu e 1 – Lending Club’s Business Model
Sou ce: Lending Club Co p Fo m 10-K o Fiscal Yea Ended Decembe 31, 2014. Secu i ies and Exchange Commission.
16
To assess c edi isk, in de eloped ma ke s lende s ypically conside his o ical loan applica ion and
loan pe o mance da a collec ed egula ly om a small numbe o sou ces based on long-s anding
banking and c edi ela ionships o de elop c edi -sco ing models. T adi ional c edi -sco ing models
applying single-pe iod classi ica ion echniques (e.g., logi , p obi ) o classi y c edi cus ome s in o
di e en isk g oups and o es ima e he p obabili y o de aul a e s ill he mos popula da a mining
echniques used in he indus y (Chamboko & B a o, 2016, 2019a,b, 2020). Indi idual classi ie s
employing single s a is ical o ope a ional esea ch me hods include linea and mul iple disc imina e
analysis (DA), logis ic eg ession (LR), p obi analysis, linea and quad a ic p og amming and da a
en elopmen analysis. Classi ie s using machine lea ning me hods such as neu al ne wo ks (NN),
suppo ec o machine (SVM), decision ees (DT), gene ic and e olu iona y algo i hms (GA), and
Bayesian Model Ensembles (BME) ha e also been in es iga ed (Zhao e al., 2017; Asho eh & B a o,
2019, 2021a,b; B a o e al., 2021; B a o & Ayuso, 2020, 2021).
Olson, Delen & Meng (2012) ca y ou an ex ensi e compa ison be ween Decision T ees and o he
con en ional da a mining echniques such as Neu al Ne wo ks and Suppo Vec o Machines in an
a emp o de elop mo e anspa en and anspo able decision suppo ools o p edic ing
bank up cy and simila compa a i e wo k is done by Huang e al. (2004).
Di ick e al. (2017) employ su i al analysis echniques o achie e highe le els o ideli y in calcula ing
a ime ho izon o when a gi en loan will de aul , hus acili a ing a e u n- ocused pe spec i e (as
opposed o bina y classi ie s which a e ubiqui ous in he ield). A simila app oach is aken by
Byanjanka (2017) o he online P2P lending ma ke whe e p omising esul s a e achie ed o
p edic ing he su i al p obabili ies o bo owe s a di e en imes.
Đu o ić (2017) analyses he p obabili y o de aul in a da ase o issued loans om online P2P lending
pla o m Lending Club, by employing a su i al analysis model o analyze he p obabili y o de aul o
loan e m leng h and pu pose. Findings yield ha he e a e iskie loan pu pose classes bu also asse
ha a longe e m is associa ed wi h a highe likelihood o de aul . Tan e al. (2018) s udy a simila
da ase and p opose an app oach elying on di e en ia ing loans based on nega i e ou comes (ei he
“cha ge-o ” o “p e-paymen ”) and, le e aging on deep neu al ne wo ks, ou pe o ming he isk
analysis capabili ies o adi ional isk assessmen capabili ies o adi ional su i al analysis
echniques.
Mezei, Byanjanka & Heikkilä (2018) use linguis ic da a ans o ma ion as a disc e iza ion s ep in
adi ional supe ised lea ning algo i hms o success ully imp o e hei classi ica ion pe o mance.
Byanjanka , Heikkilä & Mezei (2015) de elop a c edi sco ing model using A i icial Neu al Ne wo ks
(ANNs) o e alua e c edi isk in P2P lending, achie ing sa is ac o y esul s.
Mo e comp ehensi e wo ks ackle a a ie y o op ions o de e mining isk. Jin & Zhu (2015) de elop
a mo e comp ehensi e amewo k o app oaching such da ase s and combine an in oduc o y s ep o
iden i ying he de e minan s o isk h ough Random Fo es (RF) and impo ance analysis echniques
o hen se e a mul i ude o da a mining algo i hms o compa ison. They conclude ha SVMs
ou pe o m o he me hods, al hough by a ma ginal amoun . Ca michael (2014) uses a disc e e- ime
haza d model (dynamic logis ic eg ession) o es ima e he p obabili y o de aul o P2P loans. By using
a mix o bo h ha d and so in o ma ion, he model can achie e much be e esul s when compa ed
o he su eyed pla o m’s c edi g ading sys em.

17
3 METHODOLOGY
3.1 THE MODEL - LOGISTIC REGRESSION
In his wo k, we employ a logis ic eg ession model o assess he ac o s ha de e mine he loan
pe o mance o P2P loans. Logis ic eg essions model he p obabili y o an e en occu ing depending
on he alues o a se o independen a iables, p edic ing hei e ec on a bina y a iable esponse
and classi ying obse a ions by es ima ing he p obabili y ha an ou come has in a pa icula ca ego y.
This s a is ical echnique has been he mos widely used in c edi sco ing applica ions (Abdou &
Poin on, 2011) and is conside ed he indus y s anda d o de eloping c edi sco ing models (Ala’ aj &
Abbod, 2015; Lessmann, Baesens, Seow, & Thomas, 2015).
Fo his s udy, loan s a us is ou dependen a iable wi h he ollowing bina y ou come: 0 (ze o) i he
loan s a us is “Fully Paid”, meaning ha bo owe was able o epay he loan and 1 (one) i he loan
s a us is “De aul ”, meaning ha he bo owe is unable o ully pay he loan and de aul
2
. In his
sec ion, he wo k o Hosme J e al. (2013) was closely ollowed.
Conside ing he abo e, he p obabili y o a loan de aul (condi ional p obabili y) gi en he independen
a iable can be w i en as
!"
(
$!=1|(!
)
=*((!)
,
+
whe e he bo owe
,
+will de aul gi en he
in o ma ion
(!
. I his p obabili y is g ea e han 50%, he model p edic s ha he bo owe belongs o
he “De aul ” class, o he wise he model p edic s ha he bo owe ins ead belongs o he “Fully Paid”
class.
The i s s ep o de e mine his p obabili y is o es ima e a linea eg ession+ unc ion:
!(#!) = &' "($")
&'"($")= ((+ (&#!& + ⋯ + ()#!)
+ = 1, 2, … , '0
(1)0
whe e
-"
is a cons an ,
-#
is a ec o o eg ession coe icien s and
(
.
!
is a ec o o independen
a iables. Thus,
#!)
is he alue o he a iable
Χ!#
o he
,$%
bo owe , wi h
0 =1,…,3
(
3
is he
numbe o independen a iables).
Howe e , in a linea eg ession unc ion, he p obabili ies can be less han 0 and g ea e han 1, hence
he model needs o es ic he p obabili y be ween 0 and 1 by including a non-linea unc ion (logi )
on o equa ion (1) ans o ming in o logis ic eg ession unc ion (equa ion 2):
1(#!) = *#(%")
&+*#(%")
0
+ = 1, 2, … , '
0
(2)0
Ha ing he model speci ied, he nex s ep is o es ima e he coe icien s
-"
and
-#
. The me hod used
o i he non-linea model is he Maximum Likelihood Me hod (MLM). This me hod gene a es alues
o he unknown coe icien s ha maximize he p obabili y o ob aining he obse ed se o da a. The
likelihood unc ion is shown in equa ion (3).
2
Please e e o he nex chap e , Da a, o a u he enligh enmen on he a iable loan s a us
18
4(5)=61(#,)-!71 − 1(#,)81−-!
&
!'(
+
+
(3)+
I he a iable
$!
akes he alue 1 ( he bo owe de aul ), he likelihood is
*!
; i no , he likelihood is
equal o
1−*!
. To simpli y, ma hema ically, he op imiza ion o he likelihood unc ion, he loga i hm
o he equa ion is no mally maximized. The log-likelihood is de ined as:
:(5)=ln[4(5)]?3,&'71(#,)8+@1 − 3,A&'71 − 1(#,)8
&
!'(
+
+
(4)+
+
The e o e, o ind he alue o
5
ha maximizes he compu a ion
:
(
5
) we can isola e he cases
-"
and
-#
, se ing he equa ions equal o ze o and sol ing he p oblem o ob ain he desi ed esul . Due
o he nonlinea i y o logis ic eg ession equa ions, so wa e-based compu a ion me hods mus be
le e aged o compu e hem, add essing he i e a i e calcula ions equi ed.
+
3.2 PERFORMANCE MEASUREMENTS OF THE MODEL
To assess he p edic i e capaci y o ou model, we eco d and compa e i s p edic ions o he ac ual,
known, obse a ions. A T ue Posi i e (TP) occu s whene e a posi i e p edic ion (ou come equal o
“1”, o “de aul ”) made by he model coincides wi h he obse a ion, whe eas a False Posi i e (FP) is
eco ded when he model’s posi i e p edic ion is w ongly classi ied and con adic s he obse a ion.
T ue Nega i es (TN) and False Nega i es (FN) deno e a simila logic, bu o nega i e p edic ions
(ou come equal o “0”, o “ ully paid”).
Table 1 - Con usion Ma ix
Obse ed
Posi i e
Nega i e
P edic ed
Posi i e
TP
FP
Nega i e
FN
TN
Sou ce: Au ho ’s p oduc ion.
To e i y he p edic i e capaci y o ou logis ic eg ession model, i is necessa y o unde s and i s
disc imina ion unc ion. Fo his, and conside ing he abo e con usion ma ix, we can calcula e he
ollowing pe o mance measu es:
Accu acy measu es he o e all a io o co ec ly p edic ed obse a ions and can gi e an o e all iew
o he model's pe o mance. Howe e , looking a his measu e alone can build a alse pe cep ion o
he model due o i s na ow ocus.
19
BCCD"EC$ =FG+F!
FG+F!+IG+I!+
+
(5)+
Sensi i i y co esponds o he p obabili y o he co ec classi ica ion o he e en o in e es o occu ,
i.e., how well he model co ec ly classi ies ue posi i es.
JKLM,N,O,N$ =F!
F!+IG+
+
(6)+
Speci ici y is de ined as he p opo ion o obse a ions co ec ly p edic ed o belong o he nega i e
class.
JPKC,Q,C,N$ =FG
FG+I!+
+
(7)+
Al e na i e me hods o e alua e he pe o mance o a classi ica ion model wi h bina y classes include
he Recei e Ope a ing Cha ac e is ic (ROC) cu e and he A ea Unde he Cu e (AUC) (Wendle &
G ö up, 2016). The ROC shows he alues o which he e is g ea e op imiza ion o Sensi i i y as a
unc ion o Speci ici y, i.e., plo s he ue posi i e a e (TPR o Sensi i i y) o a disc e e classi ie agains
he alse posi i e a e (FPR o 1-Speci ici y). The AUC, in u n, being a unc ion o bo h he TPR and he
FPR, p o ides a measu e o he model’s abili y o di e en ia e be ween classes, o i s disc imina ion
powe – how e ec i ely i can p edic nega i e and posi i e ou comes co ec ly.
As a baseline we can unde s and how he op imal alue o AUC would be 1 – he cu e would pass by
poin (0,1), which would hen maximize AUC and gi e he op imal esul o 100% TPR and 0% FPR.
Con a ily o his, he baseline unc ion d awn o
*
(
(!
) in he ROC cu e plo p o ides he lowe bound
o a model wi hou classi ica ion powe and a calcula ed alue o 0.5 o AUC.
20
4 DATA
This chap e is di ided in o 4 sec ions. In he i s sec ion, “Da a Desc ip ion”, we p o ide a b ie , high-
le el desc ip ion o he da a collec ed. In he second pa , “Da a Re inemen ”, we p oceed o desc ibe
he s eps aken o clean up he da ase and elabo a e on how he da a e inemen was accomplished.
A e ha ea men o he s a ing da ase , we conduc an explo a o y analysis o he new, e ined,
da ase in he hi d sec ion “Explo a o y Da a Analysis”. The e we summa ize he key obse a ions on
a iables o he da ase , iden i ying ou lie s and handling missing alues. A he end o his sec ion, we
p o ide he desc ip i e s a is ics and co ela ion be ween he selec ed a iables.
4.1 DATA DESCRIPTION
The da a unde analysis we e e ie ed om he Lending Club websi e
3
, he la ges P2P lending
pla o m in he Uni ed S a es. The da a collec ed con ains in o ma ion on mo e han 2,200,000 loans
issued h ough he pla o m om 2007 o 2019. Al hough he la es obse a ions in he da ase a e
om Ma ch 2019, i con ains only loans issued un il Decembe 2018. Figu e 2 shows he numbe o
loans issued h oughou he yea s and in i , we can obse e a p ominen g ow h end in he o al
numbe o issued loans un il 2015, whe e he numbe o loans app oxima ely doubles yea - o-yea .
F om 2015 onwa ds he e is s ill an inc ease, bu i is subdued when compa ed o he p e ious ime
ame.
Figu e 2 - Numbe o loans issued by yea
Sou ce: Au ho ’s p oduc ion.
The o iginal da ase is composed o 151 a iables co e ing de ails ac oss 3 main opics, which a e
ocused on: bo owe de ails, loan cha ac e is ics and inal loan s a us (whe e a ailable). Bo owe
in o ma ion includes da a poin s such as occupa ion, employmen de ails, c edi a ing and o he
addi ional in o ma ion which p o ides Lending Club use s wi h an o e iew o who he bo owe is
3
Downloaded om h ps://www.lendingclub.com/in o/download-da a.ac ion
21
and wha hei cu en inancial/labo s a us is. Loan cha ac e is ics include de ails such as he amoun
o he loan, in e es a e, mon hly ins allmen , pu pose o he loan, and o he loan-speci ic elemen s,
ha cla i y how he loan is o be epaid. Finally, he in o ma ion p o ided on he inal loan s a us
in o ms o he la es s a us o he loan, a he ime o obse a ion (i.e. he loan can ei he be paid o ,
de aul ed, o e due o cu en ) and p o ides addi ional con ex , depending on ha same s a us – e.g.
is he e a paymen plan o he de aul ed loan. The ull desc ip ion o all 151 a iables can be ound in
Appendix A. Ou o 151 a iables, we ha e selec ed 22 independen a iables, as no all he a iables
a e ele an o his analysis. The selec ion o he a iables in he s udy is explained u he in he
ollowing sub-sec ions.
4.2 DATA REFINEMENT
In his sec ion, we desc ibe all he s eps in ol ed in he ea men o he da ase and how we ha e
p oceeded o e ine he da a he ein.
The i s s ep in ou ea men o he da ase consis s o na owing down he ime ame unde s udy.
We p oceed o il e ou he loans ha ha e no eached ma u i y ye , o gua an ee consis ency
be ween he da a poin s a ailable. Wi h his, we ha e emo ed all loans wi h a 36-mon h epaymen
e m ha we e issued a e Feb ua y 2016 and all loans wi h a 60-mon h epaymen e m ha we e
issued a e Feb ua y 2014. The loans issued in 2007 we e also emo ed as he bo owe in o ma ion
ini ially eques ed was di e en om he ollowing yea s.
As p e iously men ioned, no all he a iables a e ele an o ou s udy. Ou goal wi hin his sec ion is
o iden i y p edic o s o de aul so ha a he ime o he loan eques , he pla o m can use hose
a iables as a e e ence o decide on whe he o app o e o ejec he loan. Due o his, any a iables
ha a e no a ailable a he ime o he loan applica ion canno be used as p edic o s o c edi
app o al. Fo his eason, we emo ed a iables such as nex _pymn _d, ha dship_ lag, pymn _plan
among o he s ela ed o hese.
We lea e ou some a iables because hey ep esen he same da a as o he s, such as unded_amoun
and unded_amoun _in ha ep esen he same as loan_amn . O he a iables a e also deemed o
no be ele an as hey don’ ha e any impac on he bo owe ’s likelihood o de aul , such as id,
membe _id and policy_code, and gi en his, hey a e also emo ed. We also p oceeded o emo e
da ase columns such as emp_ i le and u l, because hey con ain oo many unique alues, making hem
di icul o ca ego ize and analyze, so we op o lea e hem ou o he s udy.
Ano he eason o dele e columns is he high equency o missing da a in some a iables. We ha e
decided o lea e ou all he a iables wi h mo e han i e pe cen (5%) o missing alues. The e o e,
we ha e le ou a iables such as m hs_since_las _ eco d and m hs_since_las _delinq.
A u he e inemen is conduc ed on he emp_leng h a iable, due o i ha ing a subs an ial olume
o missing alues. To p e en disca ding hese da a poin s (which amoun o app oxima ely 6% o he
o al da ase ), we emploied a ‘binning” echnique and g oup all missing alues in o a sepa a e g oup
labeled “missing”. Figu e 3 illus a es he dis ibu ion o he numbe o obse a ions by he leng h o
employmen .

22
Figu e 3 - Numbe o obse a ions by employmen leng h
Sou ce: Au ho ’s p oduc ion.
I can be obse ed in Figu e 3 ha he majo i y o he ca ego ies ha e a subs an ially educed numbe
o obse a ions when compa ed wi h he ca ego y “10+ yea s”. We chose o agg ega e some
ca ego ies oge he , esul ing in wo new ca ego ies (“0 - 4 yea s” and “5 - 9 yea s”). This s ep was
execu ed o simpli y he ca ego ies a ailable in a iable emp_leng h. Table 2 below shows he new
dis ibu ion o he a iable emp_leng h.
Table 2 - Summa y o he employmen leng h a iable
Missing
0 - 4 yea s
5 - 9 yea s
10+ yea s
41,645
278,428
183,607
231,919
Sou ce: Au ho ’s p oduc ion.
The only o he a iables wi h missing alues we e e ol_u il and pub_ ec_bank up cies, wi h 0.06%
and 0.11% o missing alues, espec i ely. As hese pe cen ages ep esen a o al o 1,208
obse a ions we decided o emo e hese missing alues.
As p e iously s a ed, loan s a us is he dependen a iable o his s udy and i is di ided in o six
ca ego ies desc ibed in Table 3. To adap he a iable o ou model, we ha e o ans o m i in o a
a iable con aining a bina y ou come/ alue, 0 o 1. When he loan_s a us assumes he alue o one
(1) i ep esen s he ailu e o paymen , meaning ha he bo owe is unable o ully pay he loan and
e en ually de aul s. On he o he hand, when loan_s a us assumes he alue ze o (0), his means ha
he bo owe had paid he loan, ul illing he es ablished con ac .
23
Table 3 - Numbe o loans by loan s a us
Ini ial Da a Se Dis ibu ion
Final Da a Se Dis ibu ion
Loan S a us
# o Loans
Loan S a us
# o Loans
Cha ged O
109,258
1 -
De aul
109,797
Cu en
433
0 -
Fully Paid
623,770
Does no mee he c edi
policy. S a us: Cha ged O
539
Does no mee he c edi
policy. S a us: Fully Paid
1,521
Fully Paid
622,249
In G ace Pe iod
40
La e (16-30 days)
34
La e (31-120 days)
317
To al
734,391
To al
733,567
Sou ce: Au ho ’s p oduc ion.
To p oceed wi h he ans o ma ion o he a iable, we ha e emo ed all he loans wi h “Cu en ”
s a us as he bo owe s a e s ill making mon hly paymen s and we don’ ye know he inal s a us.
Loans wi h he “In G ace Pe iod” s a us ep esen he loans ha a e a he mos 15 days wi h a la e
paymen and loan s a uses wi h names “La e (16-30 days)” and “La e (31-120 days)” ep esen he
loans ha e a delayed ins allmen be ween 16-30 and 31-120 days, espec i ely. While hese loans a e
in some way in de aul , we il e ed hem ou in his s udy as he p obabili y o hem being paid back is
signi ican (26% o 72%) compa ed wi h he “De aul ” s a us (11%) (Lending Club, 2019a).
We hen p oceeded o agg ega e he s a us “Cha ged O ” wi h “Does no mee he c edi policy.
S a us: Cha ged O ” in o a newly c ea ed s a us labeled as “De aul ”, since bo h co espond o he
same ou come. The s a us “Does no mee he c edi policy. S a us: Fully Paid” is also combined in o
he “Fully Paid” ca ego y. Table 3 shows he ini ial dis ibu ion o he numbe o loans by s a us and
he inal dis ibu ion a e he adjus men s.
The loan desc ip ion p o ided by he bo owe on why he loan is needed is con ained in he a iable
desc and i is op ional in o ma ion ha mos o he bo owe s lea e blank. The a iable by i sel is
di icul o analyze since his is a ee-s yle ield. To e ie e alue ou o his a iable, we c ea ed a
dummy a iable, w i ing_skills, ha is de i ed om he o iginal use inpu . I he bo owe has le a
desc ip ion on his ield, he o she will ha e au oma ically 10 poin s. Then, we check i he e a e any
spelling mis akes on he desc ip ion - i de ec ed, 2 poin s will be deduc ed om he ini ial 10. We also
check i he ex has no end ma ks, i.e., i i is missing ending punc ua ion - i de ec ed, ano he 2 poin s
will be deduc ed. The possible alues o w i ing_skills a e 0, i he bo owe hasn’ w i en any
desc ip ion, 6 i he e is any wo d misspelled and any missing punc ua ion, 8 i he e is any wo d
misspelled o any missing punc ua ion, and 10 i he e is a desc ip ion wi h no spelling mis akes and
wi h co ec punc ua ion.
Al hough he desc a iable is only illed in by app oxima ely 17% o bo owe s, ea lie wo ks ha e
indica ed ha his a iable may be a good p edic o o de aul (Ca michael, 2014). We employ his
24
composi ion o ex ual analysis me hods o de i e nume ic da a which is easie o analyze and o use
wi hin he con ex o ou p edic ion model.
Las ly, he da e o he ea lies c edi line opened by he bo owe (mon h and yea ) is shown in
ea lies _c _line. We ha e c ea ed a dummy a iable, c edi _his o y, whe e we ha e sub ac ed he
loan’s issue da e by he da e o he ea lies c edi line. We now ha e he numbe o yea s o c edi
his o y conside ing he da e when he bo owe ’s ea lies epo ed c edi line was opened.
The a o emen ioned e inemen s eps esul in he educ ion o a iables om a s a ing base o 151
o a o al o 25. In he sub-sec ion ha ollows we p oceed o analyze he esul ing da ase and conduc
any addi ional e inemen ha may be deemed necessa y, be o e building ou model.
4.3 EXPLORATORY DATA ANALYSIS
To u he e ine ou da ase and o p epa e i o usage in building ou p edic ion model, we ha e
conduc ed an explo a o y analysis o he p o ided da a.
We begin his explo a o y da a analysis by in es iga ing he ou lie s ha may exis on he da ase . Ou
i s inding ocuses on he a iable annual_inc. Table 4 p o ides a summa y o his a iable and a i s
glance, we can de ec some alues ha seem un ealis ic o his da a. The maximum annual income o
$9,000,000 compa ed wi h he median annual income o $62,000 and he 3 d qua ile o $89,000
sugges s ha he e a e some ou lie s. Figu e 4 shows he dis ibu ion o bo owe s’ annual income.
Obse ing Table 4 along wi h Figu e 4, we can con i m he e idence o ou lie s.
Table 4 - Summa y o he annual income a iable
Min.
1s Qu.
Median
Mean
3 d Qu.
Max.
0
45,000
62,000
73,689
89,000
9,000,000
Sou ce: Au ho ’s p oduc ion.
A deepe analysis o he emaining in o ma ion p o ided o hese obse a ions shows ha he
majo i y o he bo owe s a e eques ing small loan amoun s o deb consolida ion. Taking all hese
de ails in o accoun , we conside hese eco ds o be he esul o human e o and, he e o e, we
ha e limi ed he highes alue o annual income o $1,000,000. In o al, he e we e 139 obse a ions
emo ed.
25
Figu e 4 - Dis ibu ion o annual income
Sou ce: Au ho ’s p oduc ion.
Ano he inding conce ns he d i a iable (deb - o-income a io), which ep esen s he a io o he
bo owe ’s o al mon hly deb paymen s o e hei mon hly income. Figu e 5 displays he boxplo o
he a iable d i.
Figu e 5 - D i ou lie s
Sou ce: Au ho ’s p oduc ion.
In obse ing Figu e 5 i becomes no iceable ha he majo i y o loans a e concen a ed in he ange o
alues be ween 10 and 25 and he e is e idence o se e al ou lie s. We belie e ha hese ou lie s a e
caused by an e o as he da a p o ided would indica e ha some bo owe s ha e signi ican ly mo e
ou s anding deb han hei cu en income – we also obse ed he alue o d i o be inconsis en wi h
some o he o he da a poin s p o ided, o some bo owe s – hus, we ha e emo ed all he ou lie s.
Compa ed wi h o he s udies, o ins ance, Ca michael (2014), Emek e e al. (2015) and Möllenkamp
(2017), we can see ha a e he emo al o he ou lie s, he maximum alue o his a iable is as
expec ed. The exclusion o d i’s ou lie s dec eases ou da ase by 484 obse a ions.
32
The same happens wi h he in e es a e. This a iable is also highly signi ican on he p edic ion o he
de aul ha wi h he inc ease o he a e, he p obabili y o de aul also inc eases. This inding is
consis en wi h he indings in Figu e 6 ep esen ed in he p e ious chap e .
Loan amoun and e m a e o he a iables o be highly signi ican in p edic ing he ailu e o he loan
paymen . The posi i e sign on hese a iables sugges s ha bo owe s wi h a la ge loan amoun and
a longe pe iod o loan paymen a e mo e likely o de aul . E e y hing else equal, his p o es ha
bo owe s who eques la ge loan amoun s on a longe - e m and he e o e will ha e la ge
ins allmen s o a longe pe iod o paymen a e mo e likely o ail he paymen s.
The a iable home owne ship shows ha a bo owe paying en o owning a house is mo e likely o
de aul han a bo owe ha pays a mo gage. These esul s a e in line wi h he da a p e iously
e ealed on he explo a o y analysis whe e hese wo ca ego ies ha e a highe de aul a e han he
la e one.
Fu he o his, he wo ca ego ies ha ha e some kind o e i ica ion on he a iable e i ica ion s a us
a e also highly signi ican p edic o s. The esul s a e in line wi h he da a examined in he p e ious
chap e . This may sugges ha Lending Club e i ies he bo owe s ha hey belie e o ha e a highe
isk o de aul .
Annual income, FICO and d i a e also highly signi ican a iables. The nega i e sign on he wo i s
a iables implies ha a bo owe wi h a highe income and a g ea e FICO sco e is less likely o de aul .
Con a ily, he las o hese a iables wi h a posi i e sign indica es ha bo owe s wi h highe amoun s
o deb s uggle mo e o epay hei loans.
The bo owe ’s pu pose o eques ing a loan has 14 ca ego ies. O hose 14, 10 ca ego ies a e
signi ican a di e en le els. Bo owe s needing a loan o help hem inance hei “educa ion”, “small
business” o “medical expenses” a e iskie bo owe s han hose who a e inancing hei “wedding”,
“ aca ion” o epaying hei “c edi ca d” deb . Ac ually, he ca ego y “wedding” egis e s a nega i e
co ela ion wi h he likelihood o de aul e ealing o be he only pu pose ha a ec s nega i ely he
p obabili y o de aul wi h an OR o 0.824.
On he a iable employmen leng h, al hough he ca ego y 5-9 yea s is signi ican (a 5% le el), only
he ca ego y “missing” is highly signi ican a he 0.1% le el. Also, he la e has a coe icien wi h a
posi i e sign and OR o 1.516, ha ing a big impac on he likelihood o de aul . The ca ego y 10+ yea s,
despi e no being signi ican , is he only one om his a iable wi h a nega i e sign, ha ing he opposi e
e ec on he p obabili y o de aul . These esul s a e aligned wi h he da a in Table 12 o Appendix C.
A possible jus i ica ion o he inc eased likelihood o a bo owe de aul ing, when egis e ed unde
he “missing” ca ego y, may be due o him being unemployed and, hence, being mo e likely o de aul .
Rega ding he a iables ela ed o he c edi his o y o he bo owe , inqui ies las 6 mon hs, open
accoun s, e ol ing balance, e ol ing line u iliza ion a e and c edi his o y a e also highly signi ican .
This is in line wi h ou expec a ions, inqui ies las 6 mon hs and open accoun s show he bo owe s
lack epaymen o a loan, inc easing he p obabili y o de aul . On he o he hand, e ol ing balance,
e ol ing line u iliza ion a e, and c edi his o y show a nega i e ela ionship wi h he likelihood o
de aul o a bo owe .

33
Su p isingly, he a iable cha ged o wi hin 12 mon hs has a nega i e impac on he p obabili y o
de aul o he bo owe , i.e., he bo owe wi h mo e cha ge-o s (on o he loans) in he las 12 mon hs
is mo e likely o ully pay back he loan.
The a iable w i ing skills is highly signi ican and a ec s he p obabili y o de aul nega i ely. When
he alue o w i ing skills inc eases, he p obabili y o he bo owe paying also inc eases.
Rega ding public eco ds, he a iable seems no o ha e any signi icance on he model in es iga ed,
howe e has an odds a io o 1.011, which is highe han o he a iables wi h a highe le el o
signi icance.
5.2 MODEL PERFORMANCE
The p edic i e capaci y o he model was es ed by le e aging he emaining 33% o he sample da ase
which we e no used o building ou logis ic eg ession model (i.e. used in he “ aining” se ). We hen
p oceeded o use his “ es ing” se o e ie e a se ies o pe o mance me ics o analyze he model,
as desc ibed in sec ion 3.2.
Table 7 p o ides he summa y o he ou comes in a con ingency able.
Table 7 - Con usion Ma ix
Obse ed
Posi i e
Nega i e
P edic ed
Posi i e
207745
79
Nega i e
36292
64
Sou ce: Au ho ’s p oduc ion.
Based on he alues o he abo e able, we calcula ed he ollowing pe o mance measu es.
Table 8 - Model Pe o mance Measu es
Accu acy
0.851
Sensi i i y
0.851
Speci ici y
0.448
Sou ce: Au ho ’s p oduc ion.
The accu acy alue o he es da ase in he s udy has a e y good ou come. Ne e heless, we ha e in
mind ha his esul is dependen on he manual spli o he da a ha we did ea lie and ha his
measu e alone is no a eliable indica o o he model’s pe o mance.
Conce ning he o he wo measu es, he model pe o ms be e in dis inguishing bo owe s ha
de aul ed han bo owe s ha ully paid, as i yields be e esul s o sensi i i y han speci ici y.
Figu e 8 shows he ROC cu e o ou model. As we can obse e, he cu e is e y close o he baseline
(black line ha ep esen s he balance TPR=FPR).
34
Figu e 8 - ROC Cu e
Sou ce: Au ho ’s p oduc ion.
By e iewing he AUC indica o , i s esul e eals a alue o 0.6835, showing ha , al hough ou model
can accu a ely p edic loans ha will de aul , i is a less capable a p edic ing loans ha will no do
so. This ep esen s a lack o abili y o di e en ia e be ween he 2 classes o ou dependen a iable –
“de aul ” VS “ ully paid”.
We can conclude om he esul s and ela ed pe o mance measu es ha he model’s classi ica ion
powe is a he bes “a e age”. We belie e ha his lack o quali y may be due o he imbalanced
classi ica ion in ou dependen a iable as i is a common limi a ion wi hin loan classi ica ions and
c edi isk modeling. As pe Singh, Tsai, & Ramiah (2014), his imbalance nega i ely impac s algo i hms
such as Logis ic Reg ession ha op imizes ac oss he en i e aining se .
35
6 CONCLUSIONS
In an inc easingly inancialized wo ld, whe e new inancial echnologies and se ice models keep on
dis up ing he es ablished and adi ional p ac ices and indus ies, pee - o-pee (P2P) lending seeks o
democ a ize access o c edi o a wide ange o bo owe s (including hose deemed by con en ional
inance o be non-c edi wo hy) and also o unsecu ed consume deb , o a wide ange o in es o s.
In doing so, i is b idging a demand and a supply gap which cu en ly exis s in his la gely un apped
ma ke .
Lending Club, being one o he pionee s and bigges P2P lending pla o ms, p o ides a good objec o
s udy and aluable insigh in o he inne wo kings o hese pla o ms and hei abili y o adequa ely
p edic he isk o c edi de aul . This las unc ion u ns Lending Club (and o he analogous pla o ms)
in o mo e han jus a media o connec ing supply and demand, assigning i he esponsibili y o
cu a ing and a es ing bo owe in o ma ion and isk.
In he beginning o his wo k, we se ou o s udy a da ase o loan obse a ions om he Lending Club
pla o m, con aining in o ma ion o loans issued be ween 2007 and 2018. Wi h i we endea o ed o
u he he cu en unde s anding and es ablished knowledge o wha a e he de e minan s o loan
de aul and wha pe o mance we can expec om a p edic ion model buil a op his da a.
In o de o achie e his, we i s conduc ed a cleaning and e inemen o he da a se , elimina ing
inco ec and missing da a, whe e equi ed. This p ocess was ollowed by an explo a o y analysis o
he ou pu ed da a, o gain an o e all unde s anding o he exis ing a iables, hei co ela ion and
d i ing he selec ion o he inal a iables o be used o ou s udy o de e minan s o de aul .
To u he ou analysis, we buil a logis ic eg ession model o analyze he da ase using he s epwise
backwa d echnique in o de o a i e a a inal, model-de i ed, se o independen a iables ha we e
he mos signi ican o p edic ing loan de aul .
Ou analysis aims a es ablishing a li ecycle o e iewing and unde s anding bo owe de aul based on
ha d da a eadily a ailable o he in es o (o o P2P lending pla o ms). Unlike p io wo k, which
ocused on de eloping p edic i e models o iden i ying he de e minan s o de aul , ou wo k lays ou
and end- o-end ounda ion o unde s anding he cha ac e is ics o bo owe s loans wi hin a pla o m
(o se o loan obse a ions), iden i ying de e minan s o de aul , building a p edic i e model and
analyzing i s e icacy in p edic ing loan de aul .
Ou esul s seem o be consis en wi h exis ing li e a u e (Ca michael, 2014; Emek e e al., 2015;
Polena & Regne , 2018; Se ano-Cinca e al., 2015) and sugges ha he a iables which bes explain
(o p edic ) he bo owe s likelihood o de aul a e g ade, loan amoun , in e es a e, e m, annual
income, home owne ship, deb - o-income a io, FICO sco e, inqui ies in las 6 mon hs, numbe o open
accoun s and c edi his o y.
The mos ele an independen a iable is he g ade o a bo owe (as assigned by Lending Club) which,
ha ing he highes odds a io, p esen s he bes p edic o o loan de aul . The highe he g ade o a
gi en loan (“A” being he highes g ade and “G” he lowes , in alphabe ical o de ), he highe he
likelihood ha i will be ully epaid.
36
Finally, al hough ou model has shown a high accu acy a e o 0.8511, he AUC analysis p oduced a
esul o 0.6835, meaning ha he o e all pe o mance o ou model, as ep esen ed by i s abili y o
dis inguish be ween classes (i.e. bo owe s ha will de aul VS bo owe s ha will ully pay back a
loan) is a om excellen and, a bes , medioc e.
In summa y, al hough ou model has been able o e ec i ely p edic de aul ed loans, i s esul s ha e
yielded a signi ican amoun o “ alse nega i es”, i.e. ully paid loans ha we e p edic ed o de aul .
We ex apola e om his ha , al hough oppo uni ies may be missed o und good quali y loans, i is
bes o e on he side o cau ion when dealing wi h unsecu ed consume deb .
6.1 LIMITATIONS AND FUTURE WORK
Th oughou his wo k, we iden i ied se e al limi a ions and cons ain s ha may ha e hinde ed ou
in es iga ion and i s subsequen esul s. We p esen hose in his sec ion and also a emp o lay ou
he ounda ion o u u e wo k o build upon he esul s p o ided he ein.
Du ing he ini ial analysis and clean-up/ e inemen o ou da ase , we we e o ced o emo e a
subs an ial numbe o a iables and loan obse a ions due o missing da a o inco ec ly illed ou
o ms. This app oach, al hough consis en wi h es ablished da a analysis p ac ices, may ha e caused
he loss o po en ially aluable da a poin s o he s udy.
We hypo hesize ha he cause o his p oblem, wi h a special ocus on inco ec ly p o ided da a, is
likely ied o he ac ha hese pla o ms allow o he manual in oduc ion o da a by use s. Such
pla o ms also lack he manpowe o capaci y o handle and p ope ly e 100% o he da a which hey
ecei e.
Wi h ega ds o he esul s ob ained wi h he de eloped logis ic eg ession model, we es ima e ha
he medioc e p edic i e pe o mance, which we achie ed, is a p oduc o he da ase being
imbalanced. This is a common issue in c edi da ase s as hese will la gely con ain a disp opo iona e
amoun o non-de aul ed loans (85% o non-de aul ed loans in ou da ase ) and one ha nega i ely
impac s he p edic i e capaci y o logis ic eg ession models (Singh e al., 2014).
37
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48
8.3 APPENDIX C
Table 11 - Desc ip i e S a is ics o Nume ical Va iables
Sou ce: Au ho ’s p oduc ion.
Va iables
Mean
S d.De
Min
Median
Max
A e age by loan
s a us
Fully Paid
De aul
Annual Income
73,250
49,039
3,000
62,000
1,000,000
74,563
65,787
Cha ged O wi hin 12
mon hs
0.01
0.11
0
0
10
0.01
0.01
C edi His o y
16.12
7.56
1
15
71
16.25
15.40
Delinquency pas 2
yea s
0.31
0.86
0
0
30
0.31
0.34
Deb - o-Income
17.64
8.20
0
17.15
41.21
17.36
19.20
FICO
695
31
640
690
845
697
687
Inqui ies las 6 mon hs
0.70
1
0
0
17
0.67
0.88
In e es a e
12.42
4.17
5.32
12.29
28.99
12.06
14.47
Loan Amoun
13,202
8,090
500
11,000
35,000
13,172
13,372
Open Accoun s
11.31
5.22
1
10
84
11.28
11.48
Public Reco ds
0.20
0.59
0
0
86
0.19
0.23
Public Reco d
Bank up cies
0.12
0.36
0
0
12
0.12
0.14
Re ol ing Balance
16,032
22,095
0
11,053
2,904,836
16,289
14,573
Re ol ing line
u iliza ion a e
54.07
23.95
0
54.80
892.30
53.56
56.98
Tax Liens
0.05
0.40
0
0
85.00
0.05
0.05
W i ing Skills
1.51
3.39
0
0
10
1.50
1.53

49
Table 12 - Desc ip i e S a is ics o Ca ego ical Va iables
Va iables
# issued loans (%)
De aul a e (%)
Employmen Leng h
0 – 4 yea s
277,107
(37.8%)
15.0%
5 – 9 yea s
182,924
(25.0%
15.0%
10+ yea s
231,027
(31.5%)
13.8%
Missing
41,482
(5.7%)
21.0%
G ade
A
156,599
(21.4%)
5.5%
B
238,806
(32.6%)
11.4%
C
195,344
(26.7%)
18.5%
D
94,012
(12.8%)
24.2%
E
34,607
(4.7%)
29.8%
F
10,974
(1.5%)
34.8%
G
2,198
(0.3%)
37.9%
Home Owne ship
Mo gage
350,431
(47.8%)
12.8%
Own
73,809
(10.1%)
15.5%
Ren
308,300
(42.1%)
17.3%
Pu pose
Ca
8,371
(1.1%)
11.9%
C edi ca d
173,984
(23.8%)
12.4%
Deb consolida ion
418,877
(57.2%)
15.8%
Educa ional
361
(0.05%)
20.2%
Home imp o emen
42,686
(5.8%)
13.2%
House
3,235
(0.4%)
18.9%
Majo pu chase
15,314
(2.1%)
13.3%
Medical
7,999
(1.1%)
17.5%
Mo ing
5,195
(0.7%)
19.5%
O he
39,582
(5.4%)
17.1%
Renewable ene gy
554
(0.1%)
19.7%
Small business
9,317
(1.3%)
24.4%
Vaca ion
4,748
(0.6%)
16.1%
Wedding
2,317
(0.3%)
12.3%
Te m
36 mon hs
668,970
(91.3%)
14.0%
60 mon hs
63,570
(8.7%)
25.3%
Ve i ica ion S a us
Sou ce Ve i ied
255,845
(34.9%)
15.1%
Ve i ied
242,057
(33.0%)
18.0%
No Ve i ied
234,638
(32.0%)
11.7%
Sou ce: Au ho ’s p oduc ion.
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