Advances in Credit Risk Assessment Using Bank Statement Analytics and Automated Scoring Models

References

[1] Abisoye, A. and Akerele, J.I., 2021. A High-Impact Data-Driven Decision-Making Model for Integrating Cutting-Edge Cybersecurity Strategies into Public Policy. Governance, and Organizational Frameworks.

[2] Adekunle, B. I., Chukwuma-Eke, E. C., Balogun, E. D., & Ogunsola, K. O. (2021). Machine learning for automation: Developing data-driven solutions for process optimization and accuracy improvement. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 800–808. https://doi.org/10.54660/.IJMRGE.2021.2.1.800-808

[3] Adekunle, B. I., Chukwuma-Eke, E. C., Balogun, E. D., & Ogunsola, K. O. (2021). A predictive modeling approach to optimizing business operations: A case study on reducing operational inefficiencies through machine learning. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 791–799. https://doi.org/10.54660/.IJMRGE.2021.2.1.791-799

[4] Adekunle, B.I., Chukwuma-Eke, E.C., Balogun, E.D. and Ogunsola, K.O., 2021. Machine Learning for Automation: Developing Data-Driven Solutions for Process Optimization and Accuracy Improvement. Machine Learning, 2(1).

[5] Adekunle, B.I., Chukwuma-Eke, E.C., Balogun, E.D. and Ogunsola, K.O., 2021. Predictive Analytics for Demand Forecasting: Enhancing Business Resource Allocation Through Time Series Models.

[6] Adeleke, A. K., Igunma, T. O., & Nwokediegwu, Z. S. (2021). Modeling Advanced Numerical Control Systems to Enhance Precision in Next-Generation Coordinate Measuring Machines. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 638-649. ISSN: 2582-7138.

[7] Adewoyin, M.A., 2021. Developing frameworks for managing low-carbon energy transitions: overcoming barriers to implementation in the oil and gas industry.

[8] Agho, G., Ezeh, M.O., Isong, M., Iwe, D. and Oluseyi, K.A., 2021. Sustainable pore pressure prediction and its impact on geo-mechanical modelling for enhanced drilling operations. World Journal of Advanced Research and Reviews, 12(1), pp.540-557.

[9] Ahiadorme, J.W., Gyeke-Dako, A. and Abor, J.Y., 2018. Debt holdings and investment cash flow sensitivity of listed firms. International Journal of Emerging Markets, 13(5), pp.943-958.

[10] Alhaddad, M.M., 2018. Artificial intelligence in banking industry: a review on fraud detection, credit management, and document processing. ResearchBerg Review of Science and Technology, 2(3), pp.25-46.

[11] Arslanian, H., Fischer, F., Arslanian, H. and Fischer, F., 2019. Applications of artificial intelligence in financial services. The Future of Finance: The Impact of FinTech, AI, and Crypto on Financial Services, pp.179-197.

[12] Beck, T., 2020. Fintech and financial inclusion: Opportunities and pitfalls (No. 1165). ADBI working paper series.

[13] Berg, T., Burg, V., Gombović, A. and Puri, M., 2020. On the rise of fintechs: Credit scoring using digital footprints. The Review of Financial Studies, 33(7), pp.2845-2897.

[14] Bhatore, S., Mohan, L. and Reddy, Y.R., 2020. Machine learning techniques for credit risk evaluation: a systematic literature review. Journal of Banking and Financial Technology, 4(1), pp.111-138.

[15] Björkegren, D. and Grissen, D., 2020. Behavior revealed in mobile phone usage predicts credit repayment. The World Bank Economic Review, 34(3), pp.618-634.

[16] Boutaba, R., Salahuddin, M.A., Limam, N., Ayoubi, S., Shahriar, N., Estrada-Solano, F. and Caicedo, O.M., 2018. A comprehensive survey on machine learning for networking: evolution, applications and research opportunities. Journal of Internet Services and Applications, 9(1), pp.1-99.

[17] Casino, F., Dasaklis, T.K. and Patsakis, C., 2019. A systematic literature review of blockchain-based applications: Current status, classification and open issues. Telematics and informatics, 36, pp.55-81.

[18] Chen, Z., Van Khoa, L.D., Teoh, E.N., Nazir, A., Karuppiah, E.K. and Lam, K.S., 2018. Machine learning techniques for anti-money laundering (AML) solutions in suspicious transaction detection: a review. Knowledge and Information Systems, 57, pp.245-285.

[19] Chianumba, E. C., Ikhalea, N., Mustapha, A. Y., Forkuo, A. Y., & Osamika, D. (2021). A conceptual framework for leveraging big data and AI in enhancing healthcare delivery and public health policy. IRE Journals, 5(6), 303–305.

[20] Chukwuma-Eke, E. C., Ogunsola, O. Y., & Isibor, N. J. (2021). Designing a robust cost allocation framework for energy corporations using SAP for improved financial performance. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 809–822. https://doi.org/10.54660/.IJMRGE.2021.2.1.809-822

[21] Cobham, A. and Janský, P., 2020. Estimating illicit financial flows: A critical guide to the data, methodologies, and findings (p. 224). Oxford University Press.

[22] Conway, J.J.E., 2018. Artificial intelligence and machine learning: current applications in real estate.

[23] Croux, C., Jagtiani, J., Korivi, T. and Vulanovic, M., 2020. Important factors determining Fintech loan default: Evidence from a lendingclub consumer platform. Journal of Economic Behavior & Organization, 173, pp.270-296.

[24] Davern, M., Gyles, N., Hanlon, D. and Pinnuck, M., 2019. Is financial reporting still useful? Australian evidence. Abacus, 55(1), pp.237-272.

[25] Demerjian, P., Donovan, J. and Lewis‐Western, M.F., 2020. Income smoothing and the usefulness of earnings for monitoring in debt contracting. Contemporary Accounting Research, 37(2), pp.857-884.

[26] Deshpande, A., 2020, November. AI/ML applications and the potential transformation of Fintech and Finserv sectors. In 2020 13th CMI Conference on Cybersecurity and Privacy (CMI)-Digital Transformation-Potentials and Challenges (51275) (pp. 1-6). IEEE.

[27] Dienagha, I.N., Onyeke, F.O., Digitemie, W.N. and Adekunle, M., 2021. Strategic reviews of greenfield gas projects in Africa: Lessons learned for expanding regional energy infrastructure and security.

[28] D'Orazio, P., 2019. Income inequality, consumer debt, and prudential regulation: An agent-based approach to study the emergence of crises and financial instability. Economic Modelling, 82, pp.308-331.

[29] Doumpos, M., Lemonakis, C., Niklis, D. and Zopounidis, C., 2019. Analytical techniques in the assessment of credit risk. EURO advanced tutorials on operational research.

[30] Dugbartey, A.N., 2019. Predictive financial analytics for underserved enterprises: optimizing credit profiles and longterm investment returns. Int J Eng Technol Res Manag [Internet], 3(8), p.80.

[31] Egbuhuzor, N.S., Ajayi, A.J., Akhigbe, E.E., Agbede, O.O., Ewim, C.P.M. and Ajiga, D.I., 2021. Cloud-based CRM systems: Revolutionizing customer engagement in the financial sector with artificial intelligence. International Journal of Science and Research Archive, 3(1), pp.215-234.

[32] Elujide, I., Fashoto, S.G., Fashoto, B., Mbunge, E., Folorunso, S.O. and Olamijuwon, J.O., 2021. Application of deep and machine learning techniques for multi-label classification performance on psychotic disorder diseases. Informatics in Medicine Unlocked, 23, p.100545.

[33] Elujide, I., Fashoto, S.G., Fashoto, B., Mbunge, E., Folorunso, S.O. and Olamijuwon, J.O., 2021. Informatics in Medicine Unlocked.

[34] Elumilade, O.O., Ogundeji, I.A., Achumie, G.O., Omokhoa, H.E. and Omowole, B.M., 2021. Enhancing fraud detection and forensic auditing through data-driven techniques for financial integrity and security. Journal of Advanced Education and Sciences, 1(2), pp.55-63.

[35] Fasnacht, D., 2018. Open innovation in the financial services. In Open innovation ecosystems: Creating new value constellations in the financial services (pp. 97-130). Cham: Springer International Publishing.

[36] Feurer, M. and Hutter, F., 2019. Automated machine learning. Cham: Springer, pp.113-134.

[37] Gambacorta, L., Huang, Y., Qiu, H. and Wang, J., 2019. How do machine learning and non-traditional data affect credit scoring? New evidence from a Chinese fintech firm.

[38] Ganong, P., Jones, D., Noel, P.J., Greig, F.E., Farrell, D. and Wheat, C., 2020. Wealth, race, and consumption smoothing of typical income shocks (No. w27552). Cambridge, MA: National Bureau of Economic Research.

[39] Giudici, P., Hadji-Misheva, B. and Spelta, A., 2020. Network based credit risk models. Quality Engineering, 32(2), pp.199-211.

[40] Gomber, P., Kauffman, R.J., Parker, C. and Weber, B.W., 2018. On the fintech revolution: Interpreting the forces of innovation, disruption, and transformation in financial services. Journal of management information systems, 35(1), pp.220-265.

[41] Gozman, D., Liebenau, J. and Mangan, J., 2018. The innovation mechanisms of fintech start-ups: insights from SWIFT’s innotribe competition. Journal of Management Information Systems, 35(1), pp.145-179.

[42] Gross, T., Notowidigdo, M.J. and Wang, J., 2020. The marginal propensity to consume over the business cycle. American Economic Journal: Macroeconomics, 12(2), pp.351-384.

[43] Gupta, P. and Tham, T.M., 2018. Fintech: the new DNA of financial services. Walter de Gruyter GmbH & Co KG.

[44] Hassan, M.K., Khan, A. and Paltrinieri, A., 2019. Liquidity risk, credit risk and stability in Islamic and conventional banks. Research in International Business and Finance, 48, pp.17-31.

[45] Hassan, Y.G., Collins, A., Babatunde, G.O., Alabi, A.A. and Mustapha, S.D., 2021. AI-driven intrusion detection and threat modeling to prevent unauthorized access in smart manufacturing networks. Artificial intelligence (AI), p.16.

[46] Hoegen, A., Steininger, D.M. and Veit, D., 2018. How do investors decide? An interdisciplinary review of decision-making in crowdfunding. Electronic Markets, 28(3), pp.339-365.

[47] Huang, Y., Zhang, L., Li, Z., Qiu, H., Sun, T. and Wang, X., 2020. Fintech credit risk assessment for SMEs: Evidence from China.

[48] Kasabov, N.K., 2019. Time-space, spiking neural networks and brain-inspired artificial intelligence (Vol. 750). Berlin: Springer.

[49] Kaur, P., Sharma, M. and Mittal, M., 2018. Big data and machine learning based secure healthcare framework. Procedia computer science, 132, pp.1049-1059.

[50] Kiviat, B., 2019. The art of deciding with data: Evidence from how employers translate credit reports into hiring decisions. Socio-Economic Review, 17(2), pp.283-309.

[51] Komandla, V. and Chilkuri, B., 2019. AI and Data Analytics in Personalizing Fintech Online Account Opening Processes. Educational Research (IJMCER), 3(3), pp.1-11.

[52] Kovács, L. and Terták, E., 2019. Financial literacy theory and evidence. Bratislava, Szlovákia: Verlag Dashöfer, 150.

[53] Langevin, M., 2019. Big data for (not so) small loans: technological infrastructures and the massification of fringe finance. Review of international political economy, 26(5), pp.790-814.

[54] Leo, M., Sharma, S. and Maddulety, K., 2019. Machine learning in banking risk management: A literature review. Risks, 7(1), p.29.

[55] Liang, Y., Quan, D., Wang, F., Jia, X., Li, M. and Li, T., 2020. Financial big data analysis and early warning platform: a case study. IEEE Access, 8, pp.36515-36526.

[56] Lu, H., Li, Y., Chen, M., Kim, H. and Serikawa, S., 2018. Brain intelligence: go beyond artificial intelligence. Mobile networks and applications, 23, pp.368-375.

[57] Lwakatare, L.E., Raj, A., Crnkovic, I., Bosch, J. and Olsson, H.H., 2020. Large-scale machine learning systems in real-world industrial settings: A review of challenges and solutions. Information and software technology, 127, p.106368.

[58] Mashrur, A., Luo, W., Zaidi, N.A. and Robles-Kelly, A., 2020. Machine learning for financial risk management: a survey. Ieee Access, 8, pp.203203-203223.

[59] Meyer, A., Zverinski, D., Pfahringer, B., Kempfert, J., Kuehne, T., Sündermann, S.H., Stamm, C., Hofmann, T., Falk, V. and Eickhoff, C., 2018. Machine learning for real-time prediction of complications in critical care: a retrospective study. The Lancet Respiratory Medicine, 6(12), pp.905-914.

[60] Nwaozomudoh, M.O., Odio, P.E., Kokogho, E., Olorunfemi, T.A., Adeniji, I.E. and Sobowale, A., 2021. Developing a conceptual framework for enhancing interbank currency operation accuracy in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.481-494.

[61] Odinet, C.K., 2020. Securitizing Digital Debts. Ariz. St. LJ, 52, p.477.

[62] Odio, P.E., Kokogho, E., Olorunfemi, T.A., Nwaozomudoh, M.O., Adeniji, I.E. and Sobowale, A., 2021. Innovative financial solutions: A conceptual framework for expanding SME portfolios in Nigeria's banking sector. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), pp.495-507.

[63] Ogungbenle, H.N. and Omowole, B.M., 2012. Chemical, functional and amino acid composition of periwinkle (Tympanotonus fuscatus var radula) meat. Int J Pharm Sci Rev Res, 13(2), pp.128-132.

[64] Ogunsola, K. O., Balogun, E. D., & Ogunmokun, A. S. (2021). Enhancing financial integrity through an advanced internal audit risk assessment and governance model. International Journal of Multidisciplinary Research and Growth Evaluation, 2(1), 781–790. https://doi.org/10.54660/.IJMRGE.2021.2.1.781-790

[65] Okolie, C.I., Hamza, O., Eweje, A., Collins, A. and Babatunde, G.O., 2021. Leveraging Digital Transformation and Business Analysis to Improve Healthcare Provider Portal. IRE Journals, 4 (10), 253-254 [online]

[66] Okolie, C.I., Hamza, O., Eweje, A., Collins, A., Babatunde, G.O., & Ubamadu, B.C., 2021. Leveraging Digital Transformation and Business Analysis to Improve Healthcare Provider Portal. ICONIC RESEARCH AND ENGINEERING JOURNALS, 4(10), pp.253-257.

[67] Oluokun, O.A., 2021. Design of a Power System with Significant Mass and Volume Reductions, Increased Efficiency, and Capability for Space Station Operations Using Optimization Approaches (Doctoral dissertation, McNeese State University).

[68] Omarini, A.E., 2018. Banks and FinTechs: How to develop a digital open banking approach for the bank’s future. International Business Research, 11(9), pp.23-36.

[69] Onaghinor, O., Uzozie, O. T., Esan, O. J., Etukudoh, E. A., & Omisola, J. O. (2021). Predictive modeling in procurement: A framework for using spend analytics and forecasting to optimize inventory control. IRE Journals, 5(6), 312–314.

[70] Onaghinor, O., Uzozie, O. T., Esan, O. J., Osho, G. O., & Etukudoh, E. A. (2021). Gender-responsive leadership in supply chain management: A framework for advancing inclusive and sustainable growth. IRE Journals, 4(7), 135–137.

[71] Onaghinor, O., Uzozie, O. T., Esan, O. J., Osho, G. O., & Omisola, J. O. (2021). Resilient supply chains in crisis situations: A framework for cross-sector strategy in healthcare, tech, and consumer goods. IRE Journals, 4(11), 334–335.

[72] Onay, C. and Öztürk, E., 2018. A review of credit scoring research in the age of Big Data. Journal of Financial Regulation and Compliance, 26(3), pp.382-405.

[73] Owobu, W. O., Abieba, O. A., Gbenle, P., Onoja, J. P., Daraojimba, A. I., Adepoju, A. H., & Ubamadu, B. C. (2021). Modelling an effective unified communications infrastructure to enhance operational continuity across distributed work environments. IRE Journals, 4(12), 369–371.

[74] Owobu, W. O., Abieba, O. A., Gbenle, P., Onoja, J. P., Daraojimba, A. I., Adepoju, A. H., & Ubamadu, B. C. (2021). Review of enterprise communication security architectures for improving confidentiality, integrity, and availability in digital workflows. IRE Journals, 5(5), 370–372.

[75] Oyedokun, O.O., 2019. Green human resource management practices and its effect on the sustainable competitive edge in the Nigerian manufacturing industry (Dangote) (Doctoral dissertation, Dublin Business School).

[76] Parimi, S.S., 2018. Optimizing Financial Reporting and Compliance in SAP with Machine Learning Techniques. Available at SSRN 4934911.

[77] Parimi, S.S., 2019. Automated Risk Assessment in SAP Financial Modules through Machine Learning. Available at SSRN 4934897.

[78] Rejeb, A., Keogh, J.G., Zailani, S., Treiblmaier, H. and Rejeb, K., 2020. Blockchain technology in the food industry: A review of potentials, challenges and future research directions. Logistics, 4(4), p.27.

[79] Riikkinen, M., Saarijärvi, H., Sarlin, P. and Lähteenmäki, I., 2018. Using artificial intelligence to create value in insurance. International Journal of Bank Marketing, 36(6), pp.1145-1168.

[80] Rodriguez, L., 2020. All data is not credit data. Columbia Law Review, 120(7), pp.1843-1884.

[81] Sitotaw, G.E.T.A.H.U.N. and ADMINSTRATION, M.O.B., 2020. ASSESSMENT OF LOAN RISK AND ITS MANAGEMENT IN ADDIS CREDIT AND SAVING INSTITUTION (SC): THE CASE OF AKAKI KALITY BRANCH. Unpublished Master of Business Administration Thesis]. Addis Abba Science and Technology University.

[82] Uzowuru, I.M., Odutola, O.L.A.Y.I.N.K.A., Adetoro, A.D.E.Y.A.N.J.U., Moromoke, O.A. and Rajani, P.R.I.N.K.A., 2020. Optimized machine learning models for predictive analysis: AI-driven analytical tools for enhanced credit risk assessment. Iconic Research And Engineering Journals, 3(11), pp.321-326.

[83] Vallee, B. and Zeng, Y., 2019. Marketplace lending: A new banking paradigm?. The Review of Financial Studies, 32(5), pp.1939-1982.

[84] Wali, R.M., 2018. Analysis of Financial Ratios and Credit Risk Ratings in the Banking Industry: Insights and Findings. Journal of Business and Economic Options, 1(2), pp.52-59.

[85] Wang, F., Ding, L., Yu, H. and Zhao, Y., 2020. Big data analytics on enterprise credit risk evaluation of e-Business platform. Information Systems and e-Business Management, 18(3), pp.311-350.

[86] Weber, F.D. and Schütte, R., 2019. State-of-the-art and adoption of artificial intelligence in retailing. Digital Policy, Regulation and Governance, 21(3), pp.264-279.

[87] Wewege, L., Lee, J. and Thomsett, M.C., 2020. Disruptions and digital banking trends. Journal of Applied Finance and Banking, 10(6), pp.15-56.

[88] Xie, F., Chakraborty, B., Ong, M.E.H., Goldstein, B.A. and Liu, N., 2020. AutoScore: a machine learning–based automatic clinical score generator and its application to mortality prediction using electronic health records. JMIR medical informatics, 8(10), p.e21798.

[89] Yhip, T.M., Alagheband, B.M., Yhip, T.M. and Alagheband, B.M., 2020. Credit Analysis and Credit Management. The Practice of Lending: A Guide to Credit Analysis and Credit Risk, pp.3-46.

@article{1708077,
      author = {Oluwasola Emmanuel Adesemoye, Ezinne C. Chukwuma-Eke, Comfort Iyabode Lawal, Ngozi Joan Isibor, Abiola Oyeronke Akintobi; Florence Sophia Ezeh},
      title = {Advances in Credit Risk Assessment Using Bank Statement Analytics and Automated Scoring Models},
      journal = {Iconic Research And Engineering Journals},
      year = {2021},
      volume = {4},
      number = {9},
      pages = {234-252},
      issn = {2456-8880},
      url = {https://www.irejournals.com/formatedpaper/1708077.pdf},
      abstract = {Advancements in credit risk assessment are transforming the financial industry, with bank statement analytics and automated scoring models playing pivotal roles in enhancing the accuracy and efficiency of evaluating borrower risk. Traditionally, credit risk assessment relied on static credit scores and financial statements, which often provided limited insights into a borrower?s financial behavior. However, the use of bank statement analytics allows for a more granular analysis of a borrower?s income, spending habits, cash flow, and financial stability. This data-driven approach provides a comprehensive picture of a borrower?s financial health, enabling lenders to make more informed decisions. Automated scoring models further enhance this process by incorporating machine learning algorithms, such as decision trees, neural networks, and support vector machines, to analyze large volumes of financial data in real-time. These models integrate bank statement analytics with alternative data sources, enabling the prediction of creditworthiness with greater accuracy and speed. By automating the scoring process, financial institutions can assess risk more quickly, reduce operational costs, and scale their lending operations. Despite these advancements, challenges remain in data privacy, security, and ethical considerations, especially when incorporating alternative data sources. Additionally, automated scoring models require careful oversight to avoid issues such as model bias and lack of transparency. Future developments in technology, such as real-time data processing, AI-driven risk assessments, and the integration of blockchain for enhanced security, promise to further revolutionize credit risk assessment. This explores the impact of these innovations on the financial sector, highlighting their potential to improve decision-making, enhance credit access, and provide more accurate risk predictions in a rapidly evolving financial landscape.},
      keywords = {Credit risk assessment, Bank statement, Analytics, Automated scoring models},
      month = {March},
  }