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

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, VantageScore, 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 financial stability of lenders and borrowers.