THE ROLE OF MACHINE LEARNING IN CREDIT RISK ASSESSMENT: EMPOWERING LENDING DECISIONS
Abstract
Credit risk assessment is a critical process in the lending industry that determines the likelihood of borrowers defaulting on their credit obligations. With the emergence of machine learning algorithms, credit risk assessment has experienced a transformative shift towards more accurate and data-driven approaches. This article explores the significant role of machine learning in credit risk assessment and its impact on lending decisions. The article begins by discussing the mathematical calculations and algorithms involved in data analysis, feature extraction, predictive modeling, risk scoring, and fraud detection. Key concepts such as correlation analysis, information gain, logistic regression, support vector machines, neural networks, and ensemble learning techniques are explained. Furthermore, the evaluation metrics used to assess model performance, hyper parameter tuning techniques for optimizing models, and the estimation of the probability of default are discussed. The article concludes by highlighting the importance of credit scores derived from machine learning models in assessing creditworthiness. By leveraging these mathematical calculations and algorithms, machine learning empowers lenders to make informed decisions, improves the accuracy of credit risk assessments, and provides borrowers with fairer access to credit opportunities.
References
Jumaev G., Normuminov A., Primbetov A. 2023 Vol. 6 No. 4 (2023): JOURNAL OF MULTIDISCIPLINARY BULLETIN SAFEGUARDING THE DIGITAL FRONTIER: EXPLORING MODERN CYBERSECURITY METHODS | JOURNAL OF MULTIDISCIPLINARY BULLETIN (sirpublishers.org) https://sirpublishers.org/index.php/jomb/article/view/156
Jumaev Giyosjon, “Proceedings of the 11th International Conference on Applied Innovations in IT” XALQARO ILMIY JURNALI. ENHANCING ORGANIZATIONAL CYBERSECURITY THROUGH ARTIFICIAL INTELLIGENCE https://doi.org/10.5281/zenodo.10471793
Baesens, B., Van Gestel, T., & Verbraken, J. (2013). Handbook of data mining and knowledge discovery in business and finance. John Wiley & Sons.
Bellotti, T., & Crook, J. N. (2012). Credit scoring techniques: Theory and practice. Oxford University Press.
Chen, Y., & Guestrin, C. (2016). XGBoost: A scalable tree boosting system. ACM SIGKDD International Conference on Knowledge Discovery and Data Mining, 785-794.
Courvoisier, A., Nayak, R., & Paganini, P. (2019). Learning with limited data: An empirical comparison of supervised learning algorithms. Journal of Machine Learning Research, 20(52), 1-43.
Hardt, M., Recht, B., & Röhn, E. (2019). Machine learning and its applications. arXiv preprint arXiv:1906.08869.
Accenture. (2022). The Future of Credit Risk Management: How AI is Transforming Lending. https://www.accenture.com/us-en/services/ai-artificial-intelligence-index
McKinsey Global Institute. (2019). Artificial intelligence: The Next Digital Frontier? https://www.mckinsey.com/~/media/mckinsey/industries/advanced%20electronics/our%20insights/how%20artificial%20intelligence%20can%20deliver%20real%20value%20to%20companies/mgi-artificial-intelligence-discussion-paper.ashx
World Bank. (2020). FinTech in Development: Opportunities and Challenges for Financial Inclusion. https://www.worldbank.org/en/topic/fintech