It is time for anti-bribery: Financial institutions set the new strategic "roadmap" to mitigate illicit practices and corruption in the market
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Ragazou, Konstantina; Passas, Ioannis; Garefalakis, Alexandros Article It is time for anti-bribery: Financial institutions set the new strategic "roadmap" to mitigate illicit practices and corruption in the market Administrative Sciences Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Ragazou, Konstantina; Passas, Ioannis; Garefalakis, Alexandros (2022) : It is time for anti-bribery: Financial institutions set the new strategic "roadmap" to mitigate illicit practices and corruption in the market, Administrative Sciences, ISSN 2076-3387, MDPI, Basel, Vol. 12, Iss. 4, pp. 1-25, https://doi.org/10.3390/admsci12040166 This Version is available at: https://hdl.handle.net/10419/275436 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/
Citation: Ragazou, Konstantina, Ioannis Passas, and Alexandros Garefalakis. 2022. It Is Time for Anti-Bribery: Financial Institutions Set the New Strategic “Roadmap” to Mitigate Illicit Practices and Corruption in the Market. Administrative Sciences 12: 166. https://doi.org/10.3390/ admsci12040166 Received: 11 October 2022 Accepted: 8 November 2022 Published: 16 November 2022 Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations. Copyright: © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). administrative sciences Article It Is Time for Anti-Bribery: Financial Institutions Set the New Strategic “Roadmap” to Mitigate Illicit Practices and Corruption in the Market Konstantina Ragazou 1,2,3,* , Ioannis Passas 4and Alexandros Garefalakis 2,4 1Department of Accounting and Finance, University of Western Macedonia, GR50100 Kozani, Greece 2Department of Business Administration, Neapolis University Pafos, Paphos 8042, Cyprus 3Department of Accounting and Finance, University of Thessaly, GR41500 Larisa, Greece 4Department of Business Administration and Tourism, Hellenic Mediterranean University, GR71410 Heraklion, Greece *Correspondence: [email protected] Abstract: The financial sector is characterized by complexity due to the management of a large volume of transactions, which can lead to the difficulty of considering, identifying, and monitoring them. The lack of mechanisms in monitoring and control transactions can contribute to the development of illegal practices within a company, such as fraud, corruption, bribery, and money laundering. These phenomena can affect financial institutions negatively. Therefore, the development of an appropriate corporate governance system can ensure to members of the board and executives in a company that any illegal practice can be detected. This study aims to highlight the factors that contribute to the development of illegal practices within European financial institutions. This can help executives to plan and mitigate the illicit practices that may emerge. For this purpose, a binary logit regression analysis on panel data from 2018 to 2020 was applied to 336 European financial companies. The findings of this research emphasize the crucial role of corporate governance in the prevention of the development of illicit issues within European financial institutions, while human resources can be characterized as a pathway to corruption. Both factors, corporate governance and human resources, are main pillars of environmental, social, and corporate governance (ESG), which indicates the need of the financial sector in Europe for the elaboration of anti-corruption strategies. Thus, companies within the sector can improve their ESG score. Keywords: anti-corruption strategy; financial institutions; management; governance; human resource 1. Introduction Corruption is a phenomenon that concerns states, public mechanisms, and private companies and can be defined as dishonest behavior by those who hold a position of power, such as managers or government officials (Chan et al. 2019;Hauk et al. 2022). Corruption can include giving or accepting bribes, double-dealing, under-the-table transactions, diverting funds, or laundering money. Financial institutions are more vulnerable to corruption due to the complexity of their business and the offered products (Uberti 2021). Bribery, fraud, and blackmail are some of the most well-known forms of corruption in the finance sector. Moreover, corruption can be encountered in developing or developed countries, but the rate can differ (less or higher). Scholars have affiliated corruption with cancer, as corruption enters all aspects of society, such as political, economic, and culturalsocial, destroying all its healthy structures. Based on that, corruption is studied from economic perspectives and political and sociological science. In the international literature, corruption can be categorized by culprit or method. The usual categorization of fraud at the business level is internal and external. This classification is based on the origin of the person who committed this harmful practice, namely whether the person comes from the Adm. Sci. 2022,12, 166. https://doi.org/10.3390/admsci12040166 https://www.mdpi.com/journal/admsci
Adm. Sci. 2022,12, 166 2 of 25 internal or external environment of the business. Examples of corruption caused by people from the external environment of businesses are those beget by suppliers, customers, or other external partners. On the other hand, when an employee steals a company’s information or a manager alters its data, corruption comes from the business’s internal environment. Additionally, corruption caused by the internal environment can be considered as work fraud and illtreatment, as the person who commits it must be an employee of the company while at the same time using his capacity in the wrong way to achieve some benefit for himself. In exceptional cases, external and internal fraud may occur, as a company’s employee may cooperate with other companies to his advantage. In Europe, the fight against fraud and corruption, as well as the protection of financial interests, was formalized by creating the Anti-Fraud Coordination Unit (UCLAF) in 1988. Additionally, in 1995, the Convention on the Protection of Financial Interests of the European Communities was another action to protect the financial sector from fraud, while efforts were further strengthened with the establishment of the European Anti-Fraud Office (OLAF) in 1999. The above actions prove Europe’s willingness and effort to protect its financial interests. In essence, the above measures seek to achieve the following objectives: (i) to guarantee the protection of financial interests through the establishment of criminal law and administrative investigations, (ii) to improve OLAF’s governance and strengthen procedural safeguards in investigations, (iii) to establish and support the European Public Prosecutor’s Office (EPPO), and (iv) to reform Eurojust and to improve the protection of the Union’s financial interests. However, the pandemic of COVID-19 has favored the manifestation of corruption and fraud in the European financial sector. The restrictions against the pandemic, such as the lockdowns, have increased online transactions, limiting face-to-face interactions and identity verification. Instead, the pandemic has changed the market typologies of fraud and corruption, the most common being CEO fraud, investment scams, invoice fraud, and “phishing.” The aim of this paper is twofold: (i) to investigate the key trends and characteristics of illicit corruption in the European financial sector, and (iii) to highlight the factors that are integrated or not in the anti-corruption strategies of financial institutions as tools to mitigate illegal movements of money like bribery and money laundering. To approach the research objective, a mixed-method research design, which includes both qualitative and quantitative analysis, was applied (Figure 1). Qualitative analysis was based on bibliometric analysis, contributed to the presentation of the research trends in the studied field, and gave directions to identify the factors that drive businesses in the financial sector to illegal practices. Data for the bibliometric analysis were retrieved from the Scopus database and analyzed using the R package. The second part of the research methodology referred to developing a binary logit regression model on panel data from 2018 to 2020. The authors selected a set of variables (seventeen exogenous variables) based on the first part of the research design, which was the bibliometric analysis. Findings from regression analysis indicate the factors included or not as anti-corruption tools in European financial institutions’ campaigns to mitigate corruption in the market. The paper is broken into the following sections. Section 2presents the rigorous literature review search through the bibliometric analysis using the R package. Section 3 illustrates the materials and methods used. Section 4presents the results. Section 5discusses the findings, indicates theoretical and practical contributions, and proposes future research recommendations. Finally, Section 6concludes the paper.
Adm. Sci. 2022,12, 166 3 of 25 Adm.Sci.2022,12,xFORPEERREVIEW3of22 Figure1.Mixed‐methodsresearchdesign.Source:Ownelaboration. 2.LiteratureReview TheMainChannelsandKeyTrendsofCorruptionintheFinanceSector Corruptioncanbedefinedastheabuseofpower(publicorprivateoffice)togain personalbenefitandisasubcategoryoffraud,whichbecomesvisibleinmanywaysand forms(Figure2)(Uberti2021).Onasmallandlargescale,conflictofinterest,bribery,ve‐ nality,andfinancialblackmailarejustsomeoftheformsthatthephenomenonofcorrup‐ tiontakes(Table1)(AbbinkandWu2017). Figure2.Corruptionasaformoffraud.Source:Scopus/Biblioshiny. Table1.Distinguishingcorruptionintoforms. FormsofCorruptionDefinition ConflictofinterestsAsituationinwhichanemployeehasaprivateinterestthatcouldundulyaffecttheperfor‐ manceofhisorherdutiesandresponsibilities(Linoetal.2021). Bribery Asituationinwhichapersonintentionallyoffers,promises,orgivesanunjustifiedadvantage toanofficialorexecutiveresponsibleformakingdecisions,forhimtoactortoavoidacting,to theperformanceofhisduties(Binhadabetal.2021;Jain2020;SoansandAbe2016). Venality Apracticeinwhichbidsaremanipulatedinatender.Forexample,potentialbiddersagreeto offerhigherpricesordegradethequalityoftheproducts/servicesofferedprovidetenders amongthemselves(SoansandAbe2016). FinancialblackmailApracticethatreferstotheillegaluseofaperson’spositiontoextortmoneyinexchangeforan unfairfinancialadvantage(MangomaandWilson‐Prangley2019;Silke1998). Source:Ownelaboration. Figure 1. Mixed-methods research design. Source: Own elaboration. 2. Literature Review The Main Channels and Key Trends of Corruption in the Finance Sector Corruption can be defined as the abuse of power (public or private office) to gain personal benefit and is a subcategory of fraud, which becomes visible in many ways and forms (Figure 2) (Uberti 2021). On a small and large scale, conflict of interest, bribery, venality, and financial blackmail are just some of the forms that the phenomenon of corruption takes (Table 1) (Abbink and Wu 2017). Adm.Sci.2022,12,xFORPEERREVIEW3of22 Figure1.Mixed‐methodsresearchdesign.Source:Ownelaboration. 2.LiteratureReview TheMainChannelsandKeyTrendsofCorruptionintheFinanceSector Corruptioncanbedefinedastheabuseofpower(publicorprivateoffice)togain personalbenefitandisasubcategoryoffraud,whichbecomesvisibleinmanywaysand forms(Figure2)(Uberti2021).Onasmallandlargescale,conflictofinterest,bribery,ve‐ nality,andfinancialblackmailarejustsomeoftheformsthatthephenomenonofcorrup‐ tiontakes(Table1)(AbbinkandWu2017). Figure2.Corruptionasaformoffraud.Source:Scopus/Biblioshiny. Table1.Distinguishingcorruptionintoforms. FormsofCorruptionDefinition ConflictofinterestsAsituationinwhichanemployeehasaprivateinterestthatcouldundulyaffecttheperfor‐ manceofhisorherdutiesandresponsibilities(Linoetal.2021). Bribery Asituationinwhichapersonintentionallyoffers,promises,orgivesanunjustifiedadvantage toanofficialorexecutiveresponsibleformakingdecisions,forhimtoactortoavoidacting,to theperformanceofhisduties(Binhadabetal.2021;Jain2020;SoansandAbe2016). Venality Apracticeinwhichbidsaremanipulatedinatender.Forexample,potentialbiddersagreeto offerhigherpricesordegradethequalityoftheproducts/servicesofferedprovidetenders amongthemselves(SoansandAbe2016). FinancialblackmailApracticethatreferstotheillegaluseofaperson’spositiontoextortmoneyinexchangeforan unfairfinancialadvantage(MangomaandWilson‐Prangley2019;Silke1998). Source:Ownelaboration. Figure 2. Corruption as a form of fraud. Source: Scopus/Biblioshiny. Table 1. Distinguishing corruption into forms. Forms of Corruption Definition Conflict of interests A situation in which an employee has a private interest that could unduly affect the performance of his or her duties and responsibilities (Lino et al. 2021). Bribery A situation in which a person intentionally offers, promises, or gives an unjustified advantage to an official or executive responsible for making decisions, for him to act or to avoid acting, to the performance of his duties (Binhadab et al. 2021;Jain 2020;Soans and Abe 2016). Venality A practice in which bids are manipulated in a tender. For example, potential bidders agree to offer higher prices or degrade the quality of the products/services offered provide tenders among themselves (Soans and Abe 2016). Financial blackmail A practice that refers to the illegal use of a person’s position to extort money in exchange for an unfair financial advantage (Mangoma and Wilson-Prangley 2019;Silke 1998). Source: Own elaboration.
Adm. Sci. 2022,12, 166 4 of 25 Usually, corruption is a significant issue in public-private relations, especially in countries where states are heavily involved in the economy. However, the phenomenon of corruption is very intense in private companies. Financial corruption can be ranked first among the main sources of corruption in the economic sectors. This is based on the complexity of financial institutions’ procedures and the products they manage. A bibliometric approach was applied to analyze the data for a comprehensive overview of the trend, thematic focus, and scientific production in the field of corruption in the finance sector. Bibliometric analysis is a popular and rigorous method used for statistical evaluation to explore and analyze large volumes of scientific data (Ellegaard and Wallin 2015;Lalmi et al. 2022;Tabak et al. 2021). The aim of the bibliometric analysis is fourfold: (i) to detect state-of-the-art research for a particular field, (ii) to highlight the most cited articles and examine their impact on subsequent research by others, (iii) to show what journals, organizations, and even countries have a high impact in different fields of research, and (iv) to make comparisons (Rojas-Lamorena et al. 2022;Tabak et al. 2021). The most commonly used bibliometric methods are citation or co-citation and content analysis (Zhang et al. 2021). Regardless of the method used by the researcher, the bibliometric analysis presents a comprehensive map of the structure of knowledge, its evaluation, and measurement that focuses on the bibliographic analysis of scientific publications collected in a database (Gou et al. 2022). This article selects the Scopus database as the primary data source of the current research. Scopus is Elsevier’s bibliography and citation search services provided through the SciVerse platform. It is the most extensive database globally of references and summaries from reputable international literature with intelligent tools that help researchers retrieve, analyze, and visualize parts of the information they are interested in. It includes over 20,500 titles from 5000 publishers worldwide, 49 million subscriptions (78% with abstracts), over 5.3 million conference papers, and 100% Medline coverage. The data collection was carried out in February 2022 with the following entered search terms: [“illicitly” AND (“corruption” OR “anti-corruption”) AND “financial sector”]. The search language is English. The Scopus bibliographic citation database includes various documents, but only original articles were considered in the present analysis. A total of 687 documents were finally selected for analysis. The records for each publication retrieved during the search were converted as a Scopus BibTex file, imported into Biblioshiny, and analyzed by the Rpackage. The evolution of articles published on illicit practices and corruption in the finance sector during 2010–2021 is presented in Figure 3. The number of publications started to increase in 2013 (15 documents). Adm.Sci.2022,12,xFORPEERREVIEW4of22 Usually,corruptionisasignificantissueinpublic‐privaterelations,especiallyin countrieswherestatesareheavilyinvolvedintheeconomy.However,thephenomenon ofcorruptionisveryintenseinprivatecompanies.Financialcorruptioncanberankedfirst amongthemainsourcesofcorruptionintheeconomicsectors.Thisisbasedonthecom‐ plexityoffinancialinstitutions’proceduresandtheproductstheymanage. Abibliometricapproachwasappliedtoanalyzethedataforacomprehensiveover‐ viewofthetrend,thematicfocus,andscientificproductioninthefieldofcorruptionin thefinancesector.Bibliometricanalysisisapopularandrigorousmethodusedforstatis‐ ticalevaluationtoexploreandanalyzelargevolumesofscientificdata(Ellegaardand Wallin2015;Lalmietal.2022;Tabaketal.2021).Theaimofthebibliometricanalysisis fourfold:(i)todetectstate‐of‐the‐artresearchforaparticularfield,(ii)tohighlightthe mostcitedarticlesandexaminetheirimpactonsubsequentresearchbyothers,(iii)to showwhatjournals,organizations,andevencountrieshaveahighimpactindifferent fieldsofresearch,and(iv)tomakecomparisons(Rojas‐Lamorenaetal.2022;Tabaketal. 2021).Themostcommonlyusedbibliometricmethodsarecitationorco‐citationandcon‐ tentanalysis(Zhangetal.2021).Regardlessofthemethodusedbytheresearcher,the bibliometricanalysispresentsacomprehensivemapofthestructureofknowledge,its evaluation,andmeasurementthatfocusesonthebibliographicanalysisofscientificpub‐ licationscollectedinadatabase(Gouetal.2022).ThisarticleselectstheScopusdatabase astheprimarydatasourceofthecurrentresearch.ScopusisElsevier’sbibliographyand citationsearchservicesprovidedthroughtheSciVerseplatform.Itisthemostextensive databasegloballyofreferencesandsummariesfromreputableinternationalliterature withintelligenttoolsthathelpresearchersretrieve,analyze,andvisualizepartsofthein‐ formationtheyareinterestedin.Itincludesover20,500titlesfrom5000publishersworld‐ wide,49millionsubscriptions(78%withabstracts),over5.3millionconferencepapers, and100%Medlinecoverage.ThedatacollectionwascarriedoutinFebruary2022with thefollowingenteredsearchterms:[“illicitly”AND(“corruption”OR“anti‐corruption”) AND“financialsector”].ThesearchlanguageisEnglish.TheScopusbibliographiccita‐ tiondatabaseincludesvariousdocuments,butonlyoriginalarticleswereconsideredin thepresentanalysis.Atotalof687documentswerefinallyselectedforanalysis.Therec‐ ordsforeachpublicationretrievedduringthesearchwereconvertedasaScopusBibTex file,importedintoBiblioshiny,andanalyzedbytheRpackage. Theevolutionofarticlespublishedonillicitpracticesandcorruptioninthefinance sectorduring2010–2021ispresentedinFigure3.Thenumberofpublicationsstartedto increasein2013(15documents). Figure3.Evolutionofthenumberofarticles(2010–2021).Source:Scopus/Biblioshiny. Figure 3. Evolution of the number of articles (2010–2021). Source: Scopus/Biblioshiny.
Adm. Sci. 2022,12, 166 5 of 25 However, the graph shows that publications considerably increased with situations over the years, while 2020 can be characterized as the peak spotted year of publications on the issue. The journal with the most published articles about corruption and illicit practices in the finance sector over the period 2010–2021 is presented in Table 2. “World Development” is the journal that has already published the highest number of articles on the field of illicit practices and corruption in the finance sector (321), “American Economic Review” is the second leading journal with 210 articles found on the study field of the current paper, and the “Journal of Public Economics” (156) is the third journal among those with the most publications on financial illicit practices. Additionally, the journals included in the list with the most relevant resources in the field of financial illicit practices are indexed by the latest Academic Journal Guide 2021. Table 2. The most relevant resources in the field of illicit practices and corruption in the financial sector. Sources Number of Articles World Development (3 ***—Abs List 2021) 321 American Economic Review (4 ****—Abs List 2021) 210 Journal of Public Economics (3 ***—Abs List 2021) 156 Third World Quarterly (2**—Abs List 2021) 115 Quarterly Journal of Economics (4 ****—Abs List 2021) 109 Journal of Political Economy (4 ****—Abs List 2021) 97 Journal of Business Ethics (3 ***—Abs List 2021) 91 Journal of Economic Perspectives (4 ****—Abs List 2021) 91 Journal of Development Studies (3 ***—Abs List 2021) 89 Review of African Political Economy (2 **—Abs List 2021) 84 Review of International Political Economy (3 ***—Abs List 2021) 84 Antipode (3 ***—Abs List 2021) 75 Development and Change (3 ***—Abs List 2021) 75 Resources Policy (2 **—Abs List 2021) 75 Economic Journal (4 ****—Abs List 2021) 65 Journal of International Economics (4 ****—Abs List 2021) 65 Public Choice (3 ***—Abs List 2021) 60 Economy and Society (3 ***—Abs List 2021) 58 Journal of Comparative Economics (3 ***—Abs List 2021) 56 Journal of African Economies (2 **—Abs List 2021) 54 International Journal of Disclosure and Governance (2 **—Abs List 2021) 53 Journal of International Development (2 **—Abs List 2021) 53 Journal Of International Money and Finance (3 ***—Abs List 2021) 53 * The symbol of * refers to the journals that are indexed by ABS list. The higher the number of * the higher impact has the journal in the field. Source: Scopus/Biblioshiny. The map presented in Figure 4identifies countries’ collaboration with the leading article producers. Two areas hold a connection line indicating the status of collaboration among them. That can be understood by the scale of cooperation represented through the thickness of the red line. The United States of America and North-West Europe have deepened cooperation and exchange among scholars (Aria and Cuccurullo 2017). Moreover, the connection between Northern-Western European countries and South Africa is worthwhile to refer to, as it may have highlighted a new channel of corruption in the financial sector (Hope 2020).
Adm. Sci. 2022,12, 166 6 of 25 Adm.Sci.2022,12,xFORPEERREVIEW6of22 Figure4.Countrycollaborationmap.Source:Scopus/Biblioshiny. ThisstrongconnectionbetweenSouthAfricaandNorthern‐WesternEuropeancoun‐ tries’illicitpracticesinthefinancialsectorwasfurtheranalyzed.Figure5visualizesthe temporalstructureinatwo‐dimensionalplotoftheresearchconcentrationfrom2010to 2021basedonmultiplecorrespondenceanalyses(MCAs)ofauthorkeywords(Ariaand Cuccurullo2017).Thegraphindicatesthatthekeywordsusedinscientificoutputsare organizedintotwoprimaryclusters,whichconcentrateonrelatedissuesregardingillicit practicesandcorruptioninthefinancesector.Thetwoclustersdemonstratethediversity andintellectualthrustoftheworkundertakenineachcluster.Thesignificantdifference betweenthemisthatthefirst(color:red)highlightstheissueofcorruptionandthesecond (color:blue)highlightstheneedforanti‐corruption.Thefirstcluster(color:red)contains atotalof19keywordsassociatedwitharticlesthatemphasizethedifferentformsofcor‐ ruptioninfinancialinstitutions,suchasillicitfinancialflows,capitalflight,andmoney laundering,whicharerelatedtothestrategyofthefinancialinstitutionstoinvestin“tax haven”countries.Thisdecision‐makingallowsthemtoachievetaxavoidance,taxeva‐ sion,andincreasetheircashflow.Developingcountriesareselectedmainlybythesefi‐ nancialinstitutions.NigeriaisthetopofthefouremittersofillegalflowsinAfrica,along withSouthAfrica,theDemocraticRepublicofCongo(DRC),andEthiopia(Gillies2020; Ganda2020). Moreover,thisclusterindicatesthatrelatedresearchkeywordsofillicitpracticesand corruptionarerelatedtosustainabilityandeconomicgrowth.Thatisbasedontheissue thatcorruption,moneylaundering,andotherillegalfinancialactionsarebarriersforgov‐ ernmentstodeliversustainableeconomicgrowth(Rose2020).Specifically,Africanecon‐ omiespresentaseveredevelopmentproblembecauseoftheirroleas“taxhavens,”which preventsthemfromachievingbettereconomicperformanceandsustainability(Salahud‐ dinetal.2020).Thesecondcluster(color:blue)isencompassedbyfivekeywordsassoci‐ atedwitharticlesthataddresstheissuesofanti‐corruptionandcompliance.Moreover, beneficialownershipisvitalinthiscluster,asitisthecentralpillarofanti‐moneylaun‐ dering.Beneficialownershiptransparencyisessentialinpreventing,detecting,prosecut‐ ing,andsanctioningfinancialcrimes.Eventhoughbeneficialownershiptransparencyis increasinglyrecognized,implementationremainsuneven,andmoreclarityandgranular‐ ityarenecessary(Vian2020). Figure 4. Country collaboration map. Source: Scopus/Biblioshiny. This strong connection between South Africa and Northern-Western European countries’ illicit practices in the financial sector was further analyzed. Figure 5visualizes the temporal structure in a two-dimensional plot of the research concentration from 2010 to 2021 based on multiple correspondence analyses (MCAs) of author keywords (Aria and Cuccurullo 2017). The graph indicates that the keywords used in scientific outputs are organized into two primary clusters, which concentrate on related issues regarding illicit practices and corruption in the finance sector. The two clusters demonstrate the diversity and intellectual thrust of the work undertaken in each cluster. The significant difference between them is that the first (color: red) highlights the issue of corruption and the second (color: blue) highlights the need for anti-corruption. The first cluster (color: red) contains a total of 19 keywords associated with articles that emphasize the different forms of corruption in financial institutions, such as illicit financial flows, capital flight, and money laundering, which are related to the strategy of the financial institutions to invest in “tax haven” countries. This decision-making allows them to achieve tax avoidance, tax evasion, and increase their cash flow. Developing countries are selected mainly by these financial institutions. Nigeria is the top of the four emitters of illegal flows in Africa, along with South Africa, the Democratic Republic of Congo (DRC), and Ethiopia (Gillies 2020;Ganda 2020). Moreover, this cluster indicates that related research keywords of illicit practices and corruption are related to sustainability and economic growth. That is based on the issue that corruption, money laundering, and other illegal financial actions are barriers for governments to deliver sustainable economic growth (Rose 2020). Specifically, African economies present a severe development problem because of their role as “tax havens,” which prevents them from achieving better economic performance and sustainability (Salahuddin et al. 2020). The second cluster (color: blue) is encompassed by five keywords associated with articles that address the issues of anti-corruption and compliance. Moreover, beneficial ownership is vital in this cluster, as it is the central pillar of anti-money laundering. Beneficial ownership transparency is essential in preventing, detecting, prosecuting, and sanctioning financial crimes. Even though beneficial ownership transparency is increasingly recognized, implementation remains uneven, and more clarity and granularity are necessary (Vian 2020).
Adm. Sci. 2022,12, 166 7 of 25 Adm.Sci.2022,12,xFORPEERREVIEW7of22 Figure5.Bibliometricanalysisof687datarecords—factorialanalysis.Source:Scopus/Biblioshiny. Furthermore,ananalysisofthetrendingtopicwasconductedbasedontheauthor’s keywordsfromthedataset.Whileperformingtheanalysis,thefollowingparameterswere configured;timespanwassetfrom2010to2021,wordminimumfrequencywassetto5, thenumberofwordsperyearwassetto5,andwordlabelsizewasalsosetto5.Article keywords,whichauthorsdefine,areusuallyconnectedtosuchpublicationcontentand aresufficienttoderivetopicalaspectsofafield(AriaandCuccurullo2017).Thisanalysis givesfurtherinsightintothetrendingtopicsconcerningkeywordoccurrencesinfinancial illicitpracticesandcorruptionliteratureovertheyears.Figure6presentstheauthors’key‐ wordsinthehierarchicalarrangementoftopicsonillicitpracticesandcorruptioninthe financialsectordiscussedbyscholarsannually.Thesetopicscouldrelatetoillicitpractices infinancialinstitutionsinmanyways.Forinstance,in2019,moneylaunderingwasthe mostdiscussedtopicinthethemeofcorruptioninfinancialinstitutions.However,one yearlater,in2020,anti‐moneylaunderingwasthemostdiscussedissueinthesamefield, revealingthemarket’stransitionfromillicitpracticestosustainability(Ganda2020). Figure 5. Bibliometric analysis of 687 data records—factorial analysis. Source: Scopus/Biblioshiny. Furthermore, an analysis of the trending topic was conducted based on the author’s keywords from the dataset. While performing the analysis, the following parameters were configured; timespan was set from 2010 to 2021, word minimum frequency was set to 5, the number of words per year was set to 5, and word label size was also set to 5. Article keywords, which authors define, are usually connected to such publication content and are sufficient to derive topical aspects of a field (Aria and Cuccurullo 2017). This analysis gives further insight into the trending topics concerning keyword occurrences in financial illicit practices and corruption literature over the years. Figure 6presents the authors’ keywords in the hierarchical arrangement of topics on illicit practices and corruption in the financial sector discussed by scholars annually. These topics could relate to illicit practices in financial institutions in many ways. For instance, in 2019, money laundering was the most discussed topic in the theme of corruption in financial institutions. However, one year later, in 2020, anti-money laundering was the most discussed issue in the same field, revealing the market’s transition from illicit practices to sustainability (Ganda 2020).
Adm. Sci. 2022,12, 166 8 of 25 Adm.Sci.2022,12,xFORPEERREVIEW8of22 Figure6.Trendingtopicsbetween2014–2022.Source:Scopus/Biblioshiny. 3.MaterialsandMethods 3.1.FittingBinaryLogitRegressionModelonPanelData Inthepresentstudy,binarylogitregressionanalysiswasusedtoanalyzethefactors contributingtothepracticesofEuropeanfinancialcompanies.Abinaryresponsemodel isaregressionmodelinwhichthedependentvariableYassumesabinaryrandomvaria‐ blethattakesononlythevalueszero[0]andone[1].Inthecurrentstudy,1means“Yes” andindicatesthelikelihoodoffinancialcompaniesinEuropefallingintoillegalpractices, while0means“No”andreferstothelikelihoodofEuropeanfinancialcompaniesnotfall‐ ingintoillegalpractices.Themodelcanbespecifiedinthelogisticfunctionalformbyfirst applyingalog‐oddstransformationofyasalinearfunctionofxi,i.e., Log=y/(1−y)xβ(1) Seemingly,themodelcanbere‐specifiedinthelogisticfunctionalformas: exp EP 1 exp x x (2) wherePistheprobabilitythatfinancialcompaniesindulgeinillicitpracticesgivenavec‐ torofexplanatoryvariablesX(𝑥1,𝑥2,𝑥3,…,𝑥𝑛).Moreover,βisavectorofthecoefficients (β1,β2,β3,…,β𝑛),whileexprepresentsthenaturallogarithm. UpondividingEquation(2)byP,subtracting1,andtakingthenaturallogarithmof bothsides,thentheequationisre‐specifiedas: ln(P/1−P)=𝛼+X β +𝑢,(3) Hence,forverifyingillicitpractice,thefunctionalmodelisre‐specifiedasfollows: Figure 6. Trending topics between 2014–2022. Source: Scopus/Biblioshiny. 3. Materials and Methods 3.1. Fitting Binary Logit Regression Model on Panel Data In the present study, binary logit regression analysis was used to analyze the factors contributing to the practices of European financial companies. A binary response model is a regression model in which the dependent variable Y assumes a binary random variable that takes on only the values zero [0] and one [1]. In the current study, 1 means “Yes” and indicates the likelihood of financial companies in Europe falling into illegal practices, while 0 means “No” and refers to the likelihood of European financial companies not falling into illegal practices. The model can be specified in the logistic functional form by first applying a log-odds transformation of y as a linear function of xi, i.e., Log = y/(1 −y)xβ(1) Seemingly, the model can be re-specified in the logistic functional form as: E[P]=exp(xβ) 1+exp(xβ)(2) where P is the probability that financial companies indulge in illicit practices given a vector of explanatory variables X (x1, x2, x3, . . . , xn). Moreover, β is a vector of the coefficients ( β 1, β2, β3, ..., βn), while exp represents the natural logarithm. Upon dividing Equation (2) by P, subtracting 1, and taking the natural logarithm of both sides, then the equation is re-specified as: ln (P/1 −P) = a+ Xβ+u, (3)
Adm. Sci. 2022,12, 166 15 of 25 Table 5. Correlation matrix. BRIBCORR ESGSC SOCSC RESOURC EMISSC ENINSC WORKSC HUMRISC MANAGESC ENERGPROD RENEWA RDPERSOSC VHCN GINI HUMRES COMPUSK ITSPECWORK ICTCOMPS TRAIPERICT ICTEDU ICTTOTVA ICTSPEC INTERIND CORRIND TOTASS TOTDE TOTPROF EBIT NESAS TOTSHAST BRIBCORR 1.000 ESGSC −0.025 1.000 SOCSC −0.026 −0.747 1.000 RESOURC 0.028 −0.272 0.214 1.000 EMISSC −0.039 −0.238 0.175 −0.363 1.000 ENINSC 0.010 −0.448 0.153 −0.105 −0.019 1.000 WORKSC −0.021 −0.150 −0.279 −0.026 −0.209 0.195 1.000 HUMRISC 0.060 −0.090 −0.203 −0.197 −0.015 0.055 0.077 1.000 MANAGESC 0.025 −0.900 0.664 0.157 0.224 0.421 0.046 0.095 1.000
Adm. Sci. 2022,12, 166 16 of 25 Table 5. Cont. BRIBCORR ESGSC SOCSC RESOURC EMISSC ENINSC WORKSC HUMRISC MANAGESC ENERGPROD RENEWA RDPERSOSC VHCN GINI HUMRES COMPUSK ITSPECWORK ICTCOMPS TRAIPERICT ICTEDU ICTTOTVA ICTSPEC INTERIND CORRIND TOTASS TOTDE TOTPROF EBIT NESAS TOTSHAST ENERGPROD 0.031 0.022 −0.015 −0.021 −0.030 0.013 −0.018 0.034 −0.029 1.000 RENEWA 0.199 −0.005 0.010 −0.017 0.001 0.041 −0.065 0.037 0.024 −0.073 1.000 RDPERSOSC −0.326 −0.055 0.059 0.003 0.028 0.022 0.032 −0.047 0.033 0.104 −0.666 1.000 VHCN 0.110 0.076 −0.041 −0.083 −0.047 −0.027 −0.013 0.014 −0.055 −0.045 −0.307 −0.239 1.000 GINI −0.743 0.019 −0.028 −0.083 0.064 0.013 0.013 0.001 −0.025 0.042 0.097 0.124 −0.390 1.000 HUMRES −0.321 0.001 −0.007 −0.036 0.064 −0.009 0.007 −0.009 0.000 −0.183 0.270 −0.327 −0.005 0.282 1.000 COMPUSK 0.121 0.040 −0.002 0.076 −0.059 −0.039 −0.089 0.019 −0.020 −0.401 0.079 −0.199 0.438 −0.321 −0.255 1.000 ITSPECWORK −0.215 −0.061 0.071 0.015 0.017 0.014 0.014 −0.009 0.031 −0.249 0.011 0.139 0.134 −0.155 0.014 0.188 1.000
Adm. Sci. 2022,12, 166 17 of 25 Table 5. Cont. BRIBCORR ESGSC SOCSC RESOURC EMISSC ENINSC WORKSC HUMRISC MANAGESC ENERGPROD RENEWA RDPERSOSC VHCN GINI HUMRES COMPUSK ITSPECWORK ICTCOMPS TRAIPERICT ICTEDU ICTTOTVA ICTSPEC INTERIND CORRIND TOTASS TOTDE TOTPROF EBIT NESAS TOTSHAST ICTCOMPS 0.382 −0.004 −0.046 0.007 −0.037 0.018 0.097 −0.014 −0.004 0.223 −0.327 0.045 0.014 −0.326 −0.504 −0.298 −0.154 1.000 TRAIPERICT 0.169 0.048 −0.035 −0.074 −0.004 0.024 0.002 0.018 −0.019 0.037 0.467 −0.434 −0.197 0.204 0.244 −0.185 −0.535 −0.182 1.000 ICTEDU 0.472 0.028 −0.014 −0.070 −0.017 0.045 0.010 −0.021 −0.017 0.166 0.439 −0.247 −0.193 −0.278 0.073 −0.286 −0.200 0.241 0.496 1.000 ICTTOTVA 0.091 0.006 0.016 0.045 −0.057 0.012 −0.028 0.028 0.007 −0.208 −0.520 0.441 0.385 −0.320 −0.527 0.478 −0.025 −0.013 −0.333 −0.430 1.000 ICTSPEC −0.147 0.011 0.004 −0.005 0.039 −0.004 −0.038 −0.005 −0.005 −0.140 0.229 −0.188 −0.038 0.153 0.332 0.249 0.076 −0.425 0.491 0.244 −0.340 1.000 INTERIND 0.279 0.038 −0.007 −0.017 −0.064 0.009 −0.010 −0.014 −0.030 0.066 0.179 0.167 −0.366 0.076 −0.476 0.130 −0.529 −0.009 0.275 0.218 0.272 −0.121 1.000 CORRIND −0.378 −0.040 0.011 0.010 0.043 −0.003 0.042 −0.018 0.016 0.278 −0.381 0.267 −0.063 0.210 0.007 −0.596 0.193 0.355 −0.484 −0.357 −0.103 −0.624 −0.464 1.000 TOTASS 0.094 0.101 −0.083 0.056 −0.046 0.057 −0.125 0.084 −0.047 0.023 0.069 −0.044 0.042 −0.100 −0.013 0.108 −0.044 −0.080 0.036 0.055 0.075 −0.014 0.085 −0.123 1.000
Adm. Sci. 2022,12, 166 18 of 25 Table 5. Cont. BRIBCORR ESGSC SOCSC RESOURC EMISSC ENINSC WORKSC HUMRISC MANAGESC ENERGPROD RENEWA RDPERSOSC VHCN GINI HUMRES COMPUSK ITSPECWORK ICTCOMPS TRAIPERICT ICTEDU ICTTOTVA ICTSPEC INTERIND CORRIND TOTASS TOTDE TOTPROF EBIT NESAS TOTSHAST TOTDE −0.100 −0.107 0.088 −0.054 0.047 −0.058 0.122 −0.085 0.054 −0.025 −0.069 0.046 −0.048 0.105 0.009 −0.109 0.049 0.081 −0.037 −0.060 −0.076 0.015 −0.087 0.127 −0.999 1.000 TOTPROF −0.104 −0.040 0.019 −0.074 0.036 −0.091 0.124 −0.060 0.002 0.024 −0.086 0.061 −0.034 0.114 0.050 −0.136 0.003 0.072 −0.037 −0.026 −0.099 0.021 −0.098 0.128 −0.673 0.655 1.000 EBIT 0.049 −0.051 0.091 0.004 0.113 −0.057 −0.035 0.042 0.070 −0.025 −0.034 0.025 0.059 −0.050 0.038 −0.021 −0.009 0.002 −0.015 0.072 0.015 −0.003 −0.022 −0.021 −0.303 0.292 0.184 1.000 NESAS −0.074 −0.029 −0.027 −0.068 −0.037 −0.014 0.160 −0.051 −0.040 0.009 −0.024 −0.009 −0.051 0.097 0.006 −0.083 0.003 0.074 0.002 −0.068 −0.071 −0.006 −0.038 0.095 −0.685 0.672 0.579 −0.308 1.000 TOTSHAST 0.110 0.118 −0.101 0.000 −0.101 0.079 −0.022 −0.025 −0.133 0.045 0.023 −0.066 0.079 −0.097 0.006 0.100 −0.048 −0.052 0.026 0.033 0.057 −0.031 0.081 −0.120 0.393 −0.414 −0.478 −0.441 0.010 1.000
Adm. Sci. 2022,12, 166 19 of 25 The complete model containing all the predictors was statically significant, χ2 (29, n = 1008 ) = 166.0, p< 0.001, indicating that the model was able to distinguish between entries of illicit practices (1) and those without illicit practices (0). The R square value from the Cox and Snell model and the Nagelkerke model presented between 15% and 23% of the variation (Table 6). About nine independent variables are significant. Table 6. Binary logit regression results. R-Square Cox and Snell 0.152 Nagelkerke 0.238 Variables Coefficient S.E. Wald Sig. Odd Ratio ESGSC −0.013 0.027 0.230 0.632 0.987 SOCSC 0.008 0.014 0.276 0.600 1.008 RESOURC 0.002 0.006 0.176 0.675 1.002 EMISSC 0.007 0.005 1.752 0.186 1.007 ENINSC 0.002 0.004 0.260 0.610 1.002 WORKSC −0.002 0.008 0.094 0.759 0.998 HUMRISC 0.001 0.004 0.065 0.798 1.001 MANAGESC −0.016 0.009 2.955 0.050 1.016 ENERGPROD −0.069 0.063 1.185 0.276 0.933 RENEWA 0.017 0.028 0.379 0.538 1.017 RDPERSOSC −2.376 1.673 2.018 0.155 0.093 VBCN −0.020 0.009 4.702 0.030 0.981 GINI 0.466 0.319 2.136 0.144 1.594 HUMRES 0.166 0.064 6.702 0.010 1.181 COMPUSK −0.025 0.064 0.158 0.691 0.975 ITSPECWORK −0.098 0.057 2.919 0.038 0.907 ICTCOMPS 0.056 0.316 0.032 0.859 1.058 TRAIPERICT 0.305 0.134 5.153 0.023 1.356 ICTEDU −0.001 0.002 0.081 0.776 0.999 ICTTOTVA −0.485 0.232 4.355 0.037 0.616 ICTSPEC 1.687 0.648 6.770 0.009 5.405 INTERIND −0.100 0.036 7.755 0.005 0.905 CORRIND 0.040 0.073 0.300 0.584 1.041 TOTASS −0.006 0.009 0.459 0.498 0.994 TOTDE 0.008 0.009 0.824 0.364 1.008 TOTPROF 0.013 0.010 1.506 0.220 1.013 EBIT −0.028 0.035 0.665 0.415 0.972 NESAS −0.004 0.007 0.287 0.592 0.996 TOTSHAST −0.030 0.008 13.629 0.000 0.971 Constant −9.587 3.017 10.100 0.001 0.000 Based on the findings of the binary logit regression analysis on panel data, the main variables that significantly affect the decrease of corruption imply that an environment characterized by HUMRES, TRAIPERICT, INTERIND is more prone to corruption practices. Specifically, an area of risk for the development of corruption phenomena, which can be related to human resources (HR), concerns the mobility of employees between departments. Clear rules to deal with the phenomenon of jumping from one department to another and vice versa include waiting periods and the effective implementation of checks and transfers between the two departments, as well as the application of dissuasive sanctions in case of violation of the rules. Thence, it is important to support managers in their role as leaders in ethical issues, establish clear mandates, provide organizational support, develop an internal HR control system, and provide legal advice. Additionally, the provision of systematic training and guidance to increase awareness and develop skills related to the exercise of unpaid judgment in matters where public integrity issues may arise is equally important. On the other hand, variables of MANAGESC, VHCN, ITSPECWORK, ICTTOTVA, ICTSPEC, and TOTSHAST are not characterized as a pathway of corruption within a
Adm. Sci. 2022,12, 166 20 of 25 company. For example, MANAGESC, which indicates a company’s commitment and effectiveness towards following best practice corporate governance principles, does not significantly affect corruption. That implies that a business that has invested in issues regards the enhancement of corporate governance and the recruitment of human resources that expertise in emerging technologies can be factors that can prevent illegal activities in a financial institution. Precisely, corporate governance can guide financial institutions to increase the investors’ confidence, develop the value of the shareholders and the interested members, and confirm the equality and transparency of each interested party. Thus, the compliance of each company in the financial sector with the rules of corporate governance can contribute to the prevention of financial fraud and thereby strengthen the confidence of the investing public and long-term economic development. 5. Discussion Corruption is commonly known as any intentional and dishonest action that commits to gaining an advantage (Li and Ferreira 2011;Saha et al. 2009). It can be engaged by one or more members of the administration, those in charge of the government, officials, or third parties, which involves using deceit to gain an unfair or unlawful advantage. Corruption is a big challenge and a risk that all companies face. Although corruption is not an issue that any company wants to deal with, the reality is that most companies experience fraud to some degree (Park and Xiao 2021). Every year, massive amounts are lost due to corrupt actions in the company, which greatly affects the company’s employees, stakeholders, and business performance. Severe fraud cases can lead the company to insolvency and bankruptcy (Khan and Krishnan 2021). The phenomenon of corruption systematically occupies the global financial sector and can take various forms (Lakshmi et al. 2021). However, in recent years, the European financial sector has shown signs of improvement in the level of corruption and the transition from an informal economy to a sustainable one (Ferris et al. 2021). Although, developing appropriate strategies to combat illegal activities in the market is a significant challenge for European financial institutions. However, executives present weaknesses in analyzing and interpreting in-depth the main trends and characteristics of corruption in the market (Arayankalam et al. 2021). This study focuses on illustrating new trends and channels of economic corruption. The analysis shows the new flows of corruption channels between South Africa and the countries of North-Western Europe. Illicit practices and corruption have been at the core of discussions due to the negative impact of these harmful practices on sustainable and economic regional development. In addition, the dimension of the COVID19 pandemic has been an unfavorable condition for the budget deficit situation in some African countries, so the mobilization of domestic resources should be further strengthened, and illegal outflows addressed. Due to the vital link between North-Western Europe and South African countries, the close partnership of governments should be a priority. Europe can support African countries such as Nigeria, South Africa, the Democratic Republic of the Congo (DRC), and Ethiopia to curb illicit capital outflows and boost domestic fundraising. This can be achieved by developing a set of best practices that will make the regions of Africa sustainable and resilient. In addition, the novelty of our research is its contribution to the reduction of corruption cases in the European financial sector during the pandemic by identifying the crucial factors that can act as a pathway or not to illegal practices. The overall output of the binary logit regression model on panel data for 2018 to 2020 indicated that European financial institutions are less likely to fall into illegal practices. Regarding the findings from the analysis, the above may be based on the crucial role of good practices related to corporate governance. Corporate governance is the corporate guidance to increase investor confidence, develop shareholder value, stakeholder value, and ensure equity and transparency for all stakeholders. Therefore, the fact that the sample of this study indicates to be less vulnerable to illegal practices highlights their compliance with the rules of corporate governance, which contributes to the prevention of financial fraud,
Adm. Sci. 2022,12, 166 21 of 25 thereby strengthening the confidence of the investing public and long-term economic development. However, financial institutions should enhance their defense against illicit practices and corruption, and to achieve that, the effectiveness of corporate governance is characterized as paramount. Corporate governance should establish appropriate practices, principles, and legal system changes for all businesses to be effective. Essential factors for the effectiveness of corporate governance are the observance of the four basic principles. Specifically, all company participants should emphasize responsibility, accountability, honesty-impartiality, and transparency. Therefore, corporate governance’s effectiveness can contribute to reducing financial statement fraud. In contrast, ineffective governance can bring opposite results to customers and investors, affecting entire economies. It should be clarified that the systems, rules, and principles of corporate governance are not the same from country to country and depend on the characteristics and environment of the business activity of each country. Therefore, for corporate governance to be effective, it is necessary to establish rules and practices and create appropriate systems that will go hand in hand with the business characteristics of each country. Furthermore, findings indicate that emerging technologies are a key component of the businesses’ strategies against illicit practices and corruption and the effective promotion of transparency in all areas of the finance sector. The rapid development and spread of ICT led to the acceptance of e-governance (Adam and Fazekas 2021). The services provided through this new approach are constantly being upgraded in addition to the wealth of information and the essential elements that a business exchange daily. ICT offers access to more value-added services and transactions, thus providing comfort, efficiency, and transparency (Malanski and Póvoa 2021). Moreover, among the effects of emerging technologies included in the fight against corruption are the reduction of bureaucratic entanglements and the removal of administrative barriers, which harm entrepreneurship and damage the healthy competition of businesses. Moreover, ICT can contribute to the digitalization of the financial sector and ensure transparency. Indicatively, some policies of digitalization can be the following: (i) the wide use of electronic corporate bonds, with standardized features and lower management costs, accessible even by a smaller company, (ii) the use of so-called “smart contracts” which correspond to digital transaction protocols that automatically execute, control, and/or document legally relevant events and actions according to the terms of a contract or agreement, (iii) the use of blockchain technology (i.e., decentralized databases managed by various participants), which offers transparency in transactions and significantly reduces the risk of hacking, as well as compatibility with other platforms for future collaborations or mergers, and (iv) the use of DLT (Digital Ledger Technology), which is a technological infrastructure that allows simultaneous access, validation, and updating of documents in a reliable way over a network (Mirtsch et al. 2021;Korpysa et al. 2021). However, apart from the contribution of emerging technologies, the reforms adopted by governments in the financial sector can be important tools in the fight against fraud and corruption in the financial sector and elsewhere (Jha 2019,2020). For example, Member States have adopted actions related to reducing tax fraud and tax evasion by promoting high standards of tax governance at a global level. Based on the above insights, a proposal for future research can be the investigation of these reforms of governments and their contribution to the mitigation of corruption in the finance sector during the period of the COVID-19 pandemic. Based on the current analysis, however, a decisive factor for the development of corruption and opacity phenomena is the role of human resources. Human resources employed in science and technology have fulfilled the following conditions: a completed education at the third level but not formally qualified as above can be related to corruption issues. Thus, this is due to companies’ staffing practices of hiring people with completed education but insufficient qualifications, no moral background, and no interest in their official duties, with simultaneous non-merit selection. Additionally, the inequality in the distribution of personnel, the overlapping responsibilities, and the selection of managers
Adm. Sci. 2022,12, 166 22 of 25 based on political and party criteria because of the lack of meritocracy contribute to the spread of delinquent behaviors by human resources. Therefore, companies need to take measures that can contribute to the improvement of corporate governance, such as the implementation of appropriate management structures, providing incentives to those exercising management to act within predetermined limits, and making it clear in the business environment that there are no opportunities, nor tolerance, for the development of fraud within it (Ferris et al. 2003). The most basic measures that companies can take to improve their governance in the direction of avoiding fraud phenomena are: (i) the design and operation of an adequate internal control system, which assesses the company’s vulnerabilities and potential risks of fraud, (ii) the participation in the management of independent members of the Board of Directors, whose selection is demonstrably done through transparent procedures, (iii) the continuous training of members of the Board of Directors, Audit Committees (where they exist), and executives of the company regarding their role and the recognition of fraud elements, as well as their treatment, (iv) the adoption of a corporate governance code that is applicable, flexible, and easy to evaluate, both in its closed use as well as historically, (v) the design of regulations adapted to the operations of the company, with a gradation of approval powers, which promotes the internal communication and cooperation, especially in family businesses, in which a healthy relationship between members of the same family must also be arranged, and (vi) the development by the management of a code of ethics with a clear development of the business culture, the limits of professional behavior, and the measures to be taken in case of deviation (Guo et al. 2020;Cole et al. 2021). The code must be known to all staff. In small businesses, this code may be verbal but communicated to their members through regular meetings with management as part of healthy corporate communication. 6. Conclusions In the financial sector, there is a field whose purpose is to combat fraud perpetrated against it (Suh and Shim 2020). These people, who are positioned to prevent this phenomenon and fight it with various methods, argue that there are many concepts to determine what is fraud and what is not. Through the daily work in a financial institution, different definitions can describe fraud in the financial sector (Hilal et al. 2022). The Association of Certified Anti-Fraud Examiners defines fraud as follows: “any illegal action characterized as fraud, concealment, or breach of trust (Hilal et al. 2022;Suh and Shim 2020). These actions do not depend on the threat of violence or coerced violence. Fraud is carried out individually by each one but also by organizations with an ultimate goal of the gain of money, property, or services, meaning the avoidance of payment or loss of services or the securing of personal or professional advantage. In addition, the American Institute of Certified Public Accountants describes fraud as: “a broad legal concept distinguished from wrongdoing and depending on whether acts are intentional or not” (Bakre 2007;Papík and Papíková2022). However, regardless of which of the above concepts one can consider for fraud, it has been proved that almost all fraud incidents fall into one or two specific categories related to theft and cheating. To limit the concept of fraud to a criminal act in the financial industry, the aim of this study was to highlight the research trends in the field and the main factors that affect, positively or negatively, issues related to fraud and corruption in the sector. The binary logit regression analysis on panel data presents corporate governance and emerging technologies as the factors that can help financial institutions to mitigate corruption issues. Moreover, human resources were highlighted as the factor that can enhance corruption issues within a financial institution. To combat fraud, therefore, in financial institutions, all stakeholders must seize the opportunity to work together to strengthen the entire financial information ecosystem. Therefore, based on the findings of the current research, European financial institutions, in cooperation with the relevant bodies, should focus their strategies on two main points: (i) on strengthening corporate governance and (ii) on the systematic integration of emerging
Adm. Sci. 2022,12, 166 23 of 25 technologies into internal control procedures to avoid phenomena that are related to fraud and corruption. The achievement of strong corporate governance and standards for maintaining effective internal control over financial reporting should be a prerequisite for listing on a major stock market index, as is already the case in many countries (Yang et al. 2017). That would immediately improve standards and reduce the risk for investors. As part of the system of internal control over financial information, companies should also be required to address the risk of fraud and define clear and specific roles for each stakeholder, including management, the board of directors, the audit committee, and the internal audit (Broadstock and Chen 2020). Based on that, it is reasonable to support the application of the management’s signature on the internal control of the financial report. In addition, supervisory regulators must be strong and equipped with powers and technology. There is clear evidence of the effectiveness of strong regulatory authorities in combating corporate fraud in many parts of the world. Whether fighting fraud or financial crime, European supervisors could consider pooling their resources to pursue international fraudsters and initiate common or harmonized supervision across national markets. Author Contributions: Conceptualization, K.R.; Data curation, I.P. and A.G.; Formal analysis, I.P.; Investigation, A.G.; Methodology, K.R.; Project administration, K.R. and I.P.; Resources, I.P.; Software, K.R. and A.G.; Supervision, A.G.; Writing—original draft, K.R., I.P. and A.G.; Writing—review & editing, A.G. All authors have read and agreed to the published version of the manuscript. Funding: This research received no external funding. Institutional Review Board Statement: Not applicable. Informed Consent Statement: Not applicable. Data Availability Statement: Not applicable. Conflicts of Interest: The authors declare no conflict of interest. References Abbink, Klaus, and Kevin Wu. 2017. Reward self-reporting to deter corruption: An experiment on mitigating collusive bribery. Journal of Economic Behavior and Organization 133: 256–72. [CrossRef] Adam, Isabelle, and Mihály Fazekas. 2021. Are emerging technologies helping win the fight against corruption? A review of the state of evidence. Information Economics and Policy 57: 100950. [CrossRef] Arayankalam, Jithesh, Anupriya Khan, and Satish Krishnan. 2021. How to deal with corruption? Examining the roles of e-government maturity, government administrative effectiveness, and virtual social networks diffusion. International Journal of Information Management 58: 102203. [CrossRef] Aria, Massimo, and Corrado Cuccurullo. 2017. bibliometrix: An R-tool for comprehensive science mapping analysis. Journal of Informetrics 11: 959–75. [CrossRef] Bakre, Owolabi M. 2007. The unethical practices of accountants and auditors and the compromising stance of professional bodies in the corporate world: Evidence from corporate Nigeria. Accounting Forum 31: 277–303. [CrossRef] Binhadab, Nouf, Michael Breen, and Robert Gillanders. 2021. Press freedom and corruption in business-state interactions. Economic Systems 45: 100922. [CrossRef] Broadstock, David, and Xiaoqi Chen. 2020. Corporate site visits, private monitoring and fraud: Evidence from China. Finance Research Letters 40: 101780. [CrossRef] Chan, Kenneth S., Vinh Q. T. Dang, and Tingting Li. 2019. The evolution of corruption and development in transitional economies: Evidence from China. Economic Modelling 83: 346–63. [CrossRef] Cole, Rebel, Sofia Johan, and Denis Schweizer. 2021. Corporate failures: Declines, collapses, and scandals. Journal of Corporate Finance 67: 101872. [CrossRef] Eurostat. 2021. Enterprises That Provided Training to Develop/Upgrade ICT Skills of Their Personnel. Available online: https://ec. europa.eu/eurostat/databrowser/view/ISOC_SKE_ITTN2/default/table?lang=en&category=isoc.isoc_sk.isoc_skt (accessed on 7 November 2022). Ellegaard, Ole, and Johan A. Wallin. 2015. The bibliometric analysis of scholarly production: How great is the impact? Scientometrics 105: 1809–31. [CrossRef] Ferris, Stephen P., Jan Hanousek, and Jiri Tresl. 2021. Corporate profitability and the global persistence of corruption. Journal of Corporate Finance 66: 101855. [CrossRef] Ferris, Stephen P., Murali Jagannathan, and Adam C. Pritchard. 2003. Too Busy to Mind the Business? Monitoring by Directors with Multiple Board Appointments. Journal of Finance 58: 1087–112. [CrossRef]
Adm. Sci. 2022,12, 166 24 of 25 Ganda, Fortune. 2020. The influence of corruption on environmental sustainability in the developing economies of Southern Africa. Heliyon 6: e04387. [CrossRef] [PubMed] Gillies, Alexandra. 2020. Corruption trends during Africa’s oil boom, 2005 to 2014. Extractive Industries and Society 7: 1171–81. [CrossRef] Gou, Xiaoqing, Hui Liu, Yujie Qiang, Zhihui Lang, Haining Wang, Dong Ye, Zhiwen Wang, and Han Wang. 2022. In-depth analysis on safety and security research based on system dynamics: A bibliometric mapping approach-based study. Safety Science 147: 105617. [CrossRef] Guo, Ying, Lifang Wang, and Qian Yang. 2020. Do corporate environmental ethics influence firms’ green practice? The mediating role of green innovation and the moderating role of personal ties. Journal of Cleaner Production 266: 122054. [CrossRef] Hauk, Esther, Mónica Oviedo, and Xavier Ramos. 2022. Perception of corruption and public support for redistribution in Latin America. European Journal of Political Economy 74: 102174. [CrossRef] Hilal, Waleed, S. Andrew Gadsden, and John Yawney. 2022. Financial Fraud: A Review of Anomaly Detection Techniques and Recent Advances. Expert Systems with Applications 193. [CrossRef] Hope, Kempe Ronald. 2020. Channels of corruption in Africa: Analytical review of trends in financial crimes. Journal of Financial Crime 27: 294–306. [CrossRef] Jain, Ritika. 2020. Bribery and firm performance in India: A political economy perspective. Journal of Asian Economics 68: 101181. [CrossRef] Jha, Chandan Kumar. 2019. Financial reforms and corruption: Evidence using GMM estimation. International Review of Economics & Finance 62: 66–78. [CrossRef] Jha, Chandan Kumar. 2020. Financial Reforms and Corruption: Which Dimensions Matter? International Review of Finance 20: 515–27. [CrossRef] Khan, Anupriya, and Satish Krishnan. 2021. Moderating effects of business-systems corruption on corruption in basic national institutions and electronic government maturity: Insights from a dynamic panel data analysis. International Journal of Information Management 59: 102349. [CrossRef] Korpysa, Jarosław, Marcin Halicki, and Andreas Uphaus. 2021. New financing methods and ICT versus logistics startups. Procedia Computer Science 192: 4458–66. [CrossRef] Lakshmi, Geeta, Shrabani Saha, and Keshab Bhattarai. 2021. Does corruption matter for stock markets? The role of heterogeneous institutions. Economic Modelling 94: 386–400. [CrossRef] [PubMed] Lalmi, Abdallah, Gabriela Fernandes, and Souad Sassi Boudemagh. 2022. Synergy between Traditional, Agile and Lean management approaches in construction projects: Bibliometric analysis. Procedia Computer Science 196: 732–39. [CrossRef] Li, Dan, and Manuel Portugal Ferreira. 2011. Institutional environment and firms’ sources of financial capital in Central and Eastern Europe. Journal of Business Research 64: 371–76. [CrossRef] Lino, AndréFeliciano, Ricardo Rocha de Azevedo, AndréCarlos Busanelli de Aquino, and Ileana Steccolini. 2021. Fighting or supporting corruption? The role of public sector audit organizations in Brazil. Critical Perspectives on Accounting 83: 102384. [CrossRef] Malanski, Leonardo Köppe, and Angela Cristiane Santos Póvoa. 2021. Economic growth and corruption in emerging markets: Does economic freedom matter? International Economics 166: 58–70. [CrossRef] Mangoma, Arinao, and Anthony Wilson-Prangley. 2019. Black Tax: Understanding the financial transfers of the emerging black middle class. Development Southern Africa 36: 443–60. [CrossRef] Mirtsch, Mona, Knut Blind, Claudia Koch, and Gabriele Dudek. 2021. Information security management in ICT and non-ICT sector companies: A preventive innovation perspective. Computers and Security 109: 102383. [CrossRef] Papík, Mário, and Lenka Papíková. 2022. Detecting accounting fraud in companies reporting under US GAAP through data mining. International Journal of Accounting Information Systems 45: 100559. [CrossRef] Park, Byung Il, and Shufeng(Simon) Xiao. 2021. Doing good by combating bad in the digital world: Institutional pressures, anticorruption practices, and competitive implications of MNE foreign subsidiaries. Journal of Business Research 137: 194–205. [CrossRef] Rojas-Lamorena, Álvaro J., Salvador del Barrio-García, and Juan Miguel Alcántara-Pilar. 2022. A review of three decades of academic research on brand equity: A bibliometric approach using co-word analysis and bibliographic coupling. Journal of Business Research 139: 1067–83. [CrossRef] Rose, Kalle Johannes. 2020. Introducing the missing 11th principle of the United Nations Global Compact to reach sustainability— Follow the money . . . . Journal of Money Laundering Control 23: 355–67. [CrossRef] Saha, Shrabani, Rukmani Gounder, and Jen Je Su. 2009. The interaction effect of economic freedom and democracy on corruption: A panel cross-country analysis. Economics Letters 105: 173–76. [CrossRef] Salahuddin, Mohammad, Nick Vink, Nicholas Ralph, and Jeff Gow. 2020. Globalisation, poverty and corruption: Retarding progress in South Africa. Development Southern Africa 37: 617–43. [CrossRef] Samerei, Seyed Alireza, Kayvan Aghabayk, Nirajan Shiwakoti, and Amin Mohammadi. 2021. Using latent class clustering and binary logistic regression to model Australian cyclist injury severity in motor vehicle—Bicycle crashes. Journal of Safety Research 79: 246–56. [CrossRef]