Refining Credit Risk Analysis- Integrating Bayesian MCMC with Hamiltonian Monte Carlo

Abstract

The accurate prediction of loan defaults is paramount for financial institutions to enhance decision-making processes, optimize loan approval rates, and mitigate associated risks. This study develops a predictive model utilizing Bayesian Markov Chain Monte Carlo (MCMC) techniques to forecast potential loan defaults. Employing a comprehensive dataset of 255,000 borrower profiles, which include detailed borrower characteristics and loan information, the model integrates advanced statistical methods to assess and interpret the factors influencing loan defaults. The Bayesian framework allows for robust uncertainty quantification and model complexity management, making it particularly suitable for the nuanced nature of credit risk assessment. Results from the model demonstrate a compelling accuracy rate, substantially aligning with industry benchmarks while providing deeper insights into the probability of default as influenced by various borrower attributes. This research underscores the efficacy of Bayesian MCMC modelling in financial risk management and offers a scalable approach for financial institutions aiming to refine their credit evaluation strategies.