Refining Credit Risk Analysis- Integrating Bayesian MCMC with Hamiltonian Monte Carlo
Journal Title: International Journal of Innovative Research in Computer Science and Technology - Year 2024, Vol 12, Issue 4
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.
Authors and Affiliations
Mohit Apte
Keywords
Related Articles
- EP ID EP744925
- DOI 10.55524/ijircst.2024.12.4.14
- Views 43
- Downloads 0
Deep Learning Approach To Face Conditioning Using Invertible Conditional Generative Adversarial Networks (ICGAN)
We propose another system for evaluating generative models by means of an ill-disposed process, in which we at the same time train two models: a generative model G that catches the information conveyance, and a discrimin...
A Survey: Orthogonal Frequency Division Multiplexing
This paper is a survey regarding the orthogonal frequency division multiplexing. The requirement for high speed data transmission has increased, with the rapid growth of digital communication in recent years. UWB deliver...
Significance of the Leading Programming Languages
In today's technology era, programming is more essential than ever. Python and C++ are the most widely used programming languages. Python, the most popular programming language of the twenty-first century, is a higher-le...
Performance Comparison of Efficient Identity Based Signature Schemes
In a conventional public key crypto system, the participants must verify the certificate prior to use the public key. The main drawback of the certificate in conventional public key system are large storage, large comput...
Factor to Facilitate Learning in Knowledge Sharing
Knowledgesharing is fundamentally about making the right knowledge or the right knowledge sources (including people) available to the right people at the right time. Knowledge sharing is therefore perhaps the single most...