Peer-Reviewed Open Access Journal

IITM Journal of Management and IT

IITM Journal of Management and IT is a Bi-Annual Research Publication of Institute of Information Technology and Management.

P-ISSN: 2349-9826 English Since 2018
Current Issue

Vol. 14 No. 2 (2023)

Articles Volume 14 Issue 1 & 2 January-December 2023
DOI 10.65301/iitm.2023.14.2.438

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

Authors
112 Views
74 Downloads
Published 2023-01-30
Pages 07-13
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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