Credit risk modeling using bayesian networks

Abstract

The main goal of this research is to demonstrate how probabilistic graphs may be used for modeling and assessment of credit concentration risk. The destructive power of credit concentrations essentially depends on the amount of correlation among borrowers. However, borrower companies correlation and concentration of credit risk exposures have been difficult for the banking industry to measure in an objective way as they are riddled with uncertainty. As a result, banks do not manage to make a quantitative link to the correlation driving risks and fail to prevent concentrations from accumulating. In this paper, we argue that Bayesian networks provide an attractive solution to these problems and we show how to apply them in representing, quantifying and managing the uncertain knowledge in concentration of credits risk exposures. We suggest the stepwise Bayesian network model building and show how to incorporate expert-based prior beliefs on the risk exposure of a group of related borrowers, and then update these beliefs through the whole model with the new information. We then explore a specific graph structure, a tree-augmented Bayesian network and show that this model provides better understanding of the risk accumulating due to business links between borrowers. We also present two strategies of model assessment that exploit the measure of mutual information and show that the constructed Bayesian network is a reliable model that can be implemented to identify and control threat from concentration of credit exposures. Finally, we demonstrate that suggested tree-augmented Bayesian network is also suitable for stress-testing analysis, in particular, it can provide the estimates of the posterior risk of losses related to the unfavorable changes in the financial conditions of a group of related borrowers. C

A novel intelligent system for securing cash levels using Markov random fields

“…Bayesian methods are also widely used. In this regard, in the paper [17], the authors show how Bayesian networks may be used for the assessment of credit concentration risk by modeling the destructive power of credit concentrations using the identification of Bayesian graphical models correlations and correlations among borrowers, which are very difficult for the banking industry to calibrate. Another example is the paper [18] which represents a novel approach on banking crisis EWSs by applying dynamic Bayesian networks to systemic banking crisis since literature on these has been mainly based on the signal extraction and the logit model methods (Reviews on EWSs for predicting banking crisis are to be found in both [18] and [16]).…”

“…Consider a financial institution and let be its Branch Network. Following References [6,17], branches are the nodes and the business relationships between them are the edges, distinguishing between headquarters and branch offices in a directed graph mode. For simplicity, we consider here that the Bank Branch Network is an undirected graph where each branch is a grid node which is characterized by its cash holdings , that is, the total amount of cash allowed in the branch , denoted by .…”

An insight into the experimental design for credit risk and corporate bankruptcy prediction systems

“…Also, it is worth stressing that many studies with private data do not include a complete description of the variables that comprise the samples, and even others do not provide the database size (e.g. Pavlenko and Chernyak 2010), the number of variables (e.g. Pavlenko and Chernyak 2010;Ben-David and Frank 2009), or the proportion of samples that belong to each class of the data set (e.g.…”

“…Pavlenko and Chernyak 2010), the number of variables (e.g. Pavlenko and Chernyak 2010;Ben-David and Frank 2009), or the proportion of samples that belong to each class of the data set (e.g. Galindo and Tamayo 2000;Hoffmann et al 2002), thus making difficult to understand in depth the merits (or faults) and procedural issues of each model.…”

A Soft Intelligent Risk Evaluation Model for Credit Scoring Classification

“…Some of the most well-known and widely-used of statistical credit scoring models include logistic regression [9,10], linear discriminant analysis [11][12][13], quadratic discriminant analysis, probit regression [14], nearest neighbor analysis [15], and Bayesian network [16,17]. Although statistical models have some valuable advantages in the explanatory purpose of credit scoring, the performances of these models can not satisfy financial managers and decision makers.…”