A credit ranking model for a parafinancial company based on the ELECTRE-III method and a multiobjective evolutionary algorithm

Credit rating is an assessment performed by lenders or financial institutions to determine a person’s creditworthiness based on the proposed terms of the loan. Frequently, these institutions use rating models to obtain estimates for the probabilities of default for their clients (companies, organizations, government, and individuals) and to assess the risk of credit portfolios. Numerous statistical and data mining methods are used to develop such models. In this paper, the potential of a multicriteria decision-aiding approach is studied. As a first step, the proposed methodology models the problem as a multicriteria evaluation process with multiple and in some cases, conflicting dimensions, which are integrated to derive sound recommendation for DMs. The second step of the methodology involves building a multicriteria outranking model based on ELECTRE III method. An evolutionary algorithm is used to exploit the outranking model. The methodology is applied to a small-scale financial institution operating in the agricultural sector. We compare loan applications based on their attributes and the credit profile of the customer or credit applicant. Our methodology offers the flexibility of combining heterogeneous information together with the preferences of decision makers (DMs), generating both relative and fixed rules for selecting the best loan applications among new and existing customers, which is an improvement over traditional methods The results reveal that outranking models are well suited to credit rating, providing good ranking results and suitable understanding on the relative importance of the evaluation criteria.     

A statistical modeling methodology for the analysis of term structure of credit risk and its dependency

This paper presents a statistical modeling methodology for simultaneous estimation of the term structure for the risk-free interest rate, hazard rate, loss given default as well as credit risk dependency structure between bond-issuing industries. A model like this provides a realistic view for the market anticipation of credit risk for corporate bonds and the flexibility in capturing credit risk dependency between industries. Our statistical modeling procedure is carried out without specifying the model likelihood explicitly, and thus robust to the model mis-specification. An empirical analysis is conducted using the financial information on the Japanese bond market data. Numerical results confirm the practicality of the proposed methodology.     

A class of multi-objective expected value decision-making model with birandom coefficients and its application to flow shop scheduling problem

In this paper, we establish multi-objective decision-making models with birandom coefficients for the flow shop scheduling problem. Furthermore, we introduce the general multi-objective decision-making model under a birandom environment, and transform the birandom uncertain model into a deterministic model through an expected value operator, and some properties of the expected value model are also researched. We extend some theories of birandom variables, and especially devote ourselves to researching the expected value of two kinds of birandom variables. The expected value goal model is also proposed. In order to compute the expected value of birandom variables, birandom simulation is presented. Combined with the genetic algorithm, it is applied in dealing with objective functions and constraint functions, and to obtain the optimal solution. Finally, an application to flow shop scheduling at GEELY Haoqing automatic company is provided as an illustration.