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Volume 14 Issue 1 & 2 January-December 2023
IITM Journal of Management and IT |Articles

Articial Intelligence & Machine Learning Models for Credit Scoring and Risk Management

Himanshu Singh , Keerti , Dr. Sapna Arora
View PDF Volume 14 Issue 1 & 2 January-December 2023 10.65301/iitm.2023.14.2.438 ↗
Himanshu Singh, Keerti, Dr. Sapna Arora . (2023). Articial Intelligence & Machine Learning Models for Credit Scoring and Risk Management. IITM Journal of Management and IT, 14(2), 07-13.
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Abstract

—Credit risk management is an essential  aspect of financial management for lenders and  borrowers alike. This paper provides an overview of  credit risk management, including its measurement  and mitigation. The measurement of credit risk  involves the use of proprietary risk rating tools and  requires qualitative and quantitative techniques to  rate the risk of business borrowers. Credit risk can  be mitigated through credit structuring techniques,  sensitivity analysis, and portfolio-level controls. Basel  I, Basel II, and Basel III are rules made by the Basel  Committee on Banking Supervision to ensure banks  have enough money to cover any losses they might  have. The traditional 5C model, the FICO scoring,  Vantage Score, decision trees, logistic regression, and neural networks are among the many of the credit  scoring models addressed in the paper. Credit scores  are calculated and risks are monitored using statistical  models, credit scoring software, risk assessment tools,  data visualization tools, and credit bureau reports. The  combination of these analysis tools helps lenders and  financial institutions identify patterns and trends, assess  borrower creditworthiness, and mitigate credit risk.  This paper highlights the importance of effective credit 
risk management in ensuring the  nancial stability of 
lenders and borrowers.

Keywords

Credit Scoring Financial Risk Arti cial Intelligence (AI) Machine Learning (ML) Algorithm Models

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