International Journal of Science and Research (IJSR)

International Journal of Science and Research (IJSR)
Call for Papers | Fully Refereed | Open Access | Double Blind Peer Reviewed

ISSN: 2319-7064

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Analysis Study Research Paper | Business and Finance | United States of America | Volume 13 Issue 6, June 2024 | Rating: 4.8 / 10

Advancing Credit Risk Management: Embracing Probabilistic Graphical Models in Banking

Sanjay Moolchandani

Abstract: Assessment and management of credit risk at banks is a critical factor that ensures the stability and profitability of these institutions. Existing traditional statistical approaches that worked in the past are already proving to be incapable of coping with this new environment and the complexities intertwined within modern financial markets. Modern methodologies like probabilistic graphical models (PGMs) provide sophisticated methods for modeling these complex relationships, which integrate graph theory with probability theory. In this paper, we will explore Credit Risk and its main components, the mathematical foundation behind credit risk assessment, and the modeling techniques of these components. It compares traditional statistical models (eg, logistic regression and Monte Carlo simulations) with advanced Probabilistic Graphical models (PGMs). The paper highlights selecting the latter based on its capacity for more accurate representation of complex dependencies and uncertainties. PGMs that are covered here include Bayesian and Markov Networks, specifically for their structural representation of joint probability distributions, conditional independence as well as efficient inference. PGMs stand out for an improved model of non-linear interactions, and they allow the incorporation of uncertainty in a natural way, dynamic updating and systematic risk segmentation. This includes using Expectation-Maximization and Gradient-Based Optimization ? bringing machine learning and modern computational methods to PGMs. It exemplifies the practical use of PGMs in credit risk management with examples ranging from default probability prediction to portfolio risk assessment and real-time risk monitoring. In conclusion, this paper points to the future promise of PGMs in credit risk management through continuing advancements in computation facilitated by embedding within an ML/AI framework. Reimagining This transformation will revolutionize how financial institutions measure and predict risks.

Keywords: Credit Risk Management, Probabilistic Graphical Models (PGMs), Bayesian Networks, Markov Networks, Logistic Regression, Monte Carlo Simulations, Machine Learning, Artificial Intelligence

Edition: Volume 13 Issue 6, June 2024,

Pages: 74 - 80

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