Bio-Inspired Credit Risk Analysis

Credit risk analysis has attracted much attention from financial institutions due to the recent financial crises and regulatory concerns of Basel II (Yu et al., 2008). Furthermore, business competition for obtaining more market share and profit become more and more aggressive in recent years, some institutions take more risks to achieve competitive advantage in the market. Consequently, many financial institutions suffered a great loss from a steady increase of defaults and bad loans from their counterparties. In USA, the general credit cards issuers charged off 27.19 billion in debt as a loss in 1997 and this figure had reached $31.91 billion in 2006 (HSN Consultants Inc., 2007). However, more and more adult population use credit products, such as mortgages, car loan, and credit card, etc., from banks or other financial institutions. For the financial institutions, they can not refuse such a large credit market to averse the credit risk. Therefore, an effective credit risk analysis model has been a crucial factor because an effective credit risk analysis technique would be transformed into significant future savings.

The remainder of this chapter is organized as follows. In next section, we explain how literatures were selected. Section 1.3 examines and analyzes 32 articles in detail and investigates their quantitative methods and classification accuracy. In addition, the performance of 12 articles using support vector machines will be reported in detail. The main factors that affect the performance of the SVM will be explored in this section. Subsequently, some implications and future research directions are pointed out in Section 1.4. Finally, Section 1.5 concludes the chapter.

Credit risk assessment has become an increasingly important area for financial institutions, especially for banks and credit card companies. In the history of financial institutions, some biggest failures were related to credit risk, such as the 1974 failure of Herstatt Bank (Philippe, 2003). In recent years, many financial institutions suffered a great loss from a steady increase of defaults and bad loans from their counterparties. So, for the credit-granting institution, the ability to accurately discriminate the good counterparties and the bad ones has become crucial. In the credit industries, the quantitative credit scoring model has been developed for this task in many years, whose main idea is to classify the credit applicants to be good or bad according to their characters (age, income, job status, etc.) by the model built on the massive information on previous applicants’ characters and their subsequent performance.

The rest of this chapter is organized as follows. Section 2.2 briefly introduces the SVM with the NPA algorithm. In Section 2.3, the parameter selection technology based on DOE is briefly discussed and the hybrid algorithm of NPA and the parameter selection is described. The results of the algorithm’s testing on a real-life dataset and comparisons with other methods are discussed in Section 2.4. Section 2.5 gives a short conclusion about the chapter.

With the rapid growth and increased competition in credit industry, credit risk evaluation is becoming more important for credit-granting institutions. A good credit risk evaluation tool can help them to grant credit to more creditworthy applicants thus increasing profits. Moreover, it can deny credit for the noncreditworthy applicants and thus decreasing losses. Currently the credit-granting institutions are paying much more attention to develop efficient and sophisticated tools to evaluate and control credit risks, which can help them to win more market shares without taking too much risk. In recent two decades, credit scoring is becoming one of the primary methods to develop a credit risk assessment tool.

The main purpose of this chapter is to propose the LSSVM-based credit scoring models with direct search method for parameters selection. The rest of this chapter is organized as follows. In Section 3.2, the LSSVM and DS methodology are described briefly. Section 3.3 presents a computational experiment to demonstrate the effectiveness and efficiency of the model and simultaneously we compared the performance between the DS and DOE, GA, and GS methods. Section 3.4 gives concluding remarks.

Different from the previous studies, a novel hybrid intelligent mining system is designed by hybridizing rough sets and support vector machines from a new perspective. In the proposed system, original information table is firstly reduced by rough sets from two-dimensional (attribute dimension and object dimension) reduction (2D-Reduction) view, and then support vector machines are used to extract typical features and to filter its noise and thus reduce the information table further. The goal of first step (i.e., 2D-Reduction) is to reduce the training burden and accelerate the learning process for support vector machines. Finally, the mined knowledge or classification rule sets are generated from the reduced information table by rough sets, rather than from the trained support vector machines. Therefore, the advantage of our proposed hybrid intelligent system is that it can overcome difficulty of extracting rules from a training support vector machine and possess the robustness which is lacking for rough set based approaches. To illustrate the effectiveness of the proposed system, two publicly credit datasets including both consumer and corporation credits are used.

The rest of the chapter is organized as follows. Section 4.2 describes some preliminaries about rough sets and support vector machine. In Section 4.3, the proposed hybrid intelligent mining system incorporating SVM into rough set is described and the algorithms to generate classification rules from information table are proposed. In Section 4.4, we compare and analyze some empirical results about two real-world credit datasets. Finally, some conclusions are drawn in Section 4.5.

In this chapter, we introduce a new credit risk classification technique, least squares fuzzy SVM (LS-FSVM), to discriminate good creditors from bad ones. The fuzzy SVM (FSVM) was first proposed by Lin and Wang (2002) and it has more suitability in credit risk assessment. The main reason is that in credit risk assessment areas we usually cannot label one customer as absolutely good who is sure to repay in time, or absolutely bad who will default certainly, the FSVM treats every sample as both positive and negative classes with the fuzzy membership. By this way the FSVM will have more generalization ability, while preserving the merit of insensitive to outliers. Although the FSVM has good generalization capability, the computational complexity of the existing FSVM is rather difficult because the final solution is derived from a quadratic programming (QP) problem. For reducing the complexity, this chapter proposes a least squares solution to FSVM. In the proposed model, we consider equality constraints instead of inequalities for the classification problem with a formulation in least squares sense. As a result the solutions follow directly from solving a set of linear equations, instead of QP from the classical FSVM approach (Lin and Wang, 2002), thus reducing the computational complexity relative to the classical FSVM. The main motivation of this chapter is to formulate a least squares version of FSVM for binary classification problems and to apply it to the credit risk evaluation field and meantime, to compare its performance with several typical credit risk assessment techniques.

The rest of this chapter is organized as follows. Section 5.2 illustrates the methodology formulation of LS-FSVM. In Section 5.3, we use a realworld credit dataset to test the classification potential of the LS-FSVM. Section 5.4 concludes the chapter.

Extant evidence shows that in the past two decades bankruptcies and defaults have occurred at higher rates than at any time. Due to recent financial crises and regulatory concerns, credit risk assessment is an area that has seen a resurgence of interest from both the academic world and the business community. Especially for credit-granting institutions, such as commercial banks and some credit card companies, the ability to discriminate faithful customers from bad ones is crucial. In order to enable the interested parties to take either preventive or corrective action, the need for efficient and reliable models that predict defaults accurately is imperative.

The general approach of credit risk analysis is to apply a classification technique on similar data of previous customers – both faithful and delinquent customers – in order to find a relation between the characteristics and potential failure. Accurate classifiers should be found in order to categorize new applicants or existing customers as good or bad.

In the seminal paper, Fisher (1936) attempted to find a linear classifier that best differentiates between the two groups of satisfactory and unsatisfactory customers based on statistical discriminant analysis. Nonlinear regression models, logistic regression (Wiginton, 1980) and probit regression (Grablowsky and Talley, 1981), also have been applied in credit risk analysis.

The main motivation of this chapter is to propose a new evolving LSSVM learning paradigm integrating LSSVM with GA for evaluating credit risk and to test the predictability of the proposed learning paradigm by comparing it with statistical models and neural network models. The rest of the chapter is organized as follows. The next section gives a brief introduction of SVM and LSSVM. The new evolving LSSVM learning paradigm is described in Section 7.3 in detail. In Section 7.4 the research data and comparable classification models are presented. The experimental results are reported in Section 7.5. Section 7.6 concludes the chapter.

At the beginning of 2005, the total outstanding consumer credit reached $2.127 trillion in U.S. Among them $0.801 trillion is revolving and $1.326 trillion is non-revolving. According to data released by the Administrative Office of the U.S. Courts, in the year that ended on December 31, 2003, U.S. bankruptcy filings set a record level, totaling 1,660,245, which included 1,625,208 non-business and 35,037 business bankruptcy filings. Also 2,062,000 people filed bankruptcy in the year that ended on December 31, 2004. Similarly, lack of a robust credit rating model has been an important issue that slowed down the development of complicated products, such as, credit derivatives, in some Asia countries, which made it difficult for investors and firms to find suitable instruments to transfer the credit risks they faced.

The motivation of this chapter is to formulate a multistage reliabilitybased SVM ensemble learning paradigm for credit risk evaluation and compare its performance with other existing credit risk assessment techniques. The rest of the chapter organized as follows. The next section presents a literature review about credit risk evaluation models and techniques. In Section 8.3, an overall formulation process of the multistage SVM ensemble learning model is provided in detail. To verify the effectiveness of the proposed method, two real examples are performed and accordingly the experiment results are reported in Section 8.4. And Section 8.5 concludes the chapter.

Credit risk assessment is a crucial decision for financial institutions due to high risks associated with inappropriate credit decisions that may lead to huge amount of losses. It is an even more important task today as financial institutions have been experiencing serious challenges and competition during the past decades. When considering the case regarding the application for a large loan, such as a mortgage or a construction loan, the lender tends to use the direct and individual scrutiny by a loan officer or even a committee. However, if hundreds of thousands, even millions of credit card or consumer loan applications need to be evaluated, the financial institutions will usually adopt models to assign scores to applicants rather than examining each one in detail. Hence various credit scoring models need to be developed for the purpose of efficient credit approval decisions (Lee and Chen, 2005).

The main motivation of this chapter is to take full advantage of the good generalization capability of SVM and inherent parallelism of metalearning to design a powerful credit risk evaluation system. The rest of this chapter is organized as follows. Section 9.2 describes the building process of the proposed SVM-based metamodeling technique in detail. For illustration and verification purposes, one publicly used credit dataset is used and the empirical results are reported in Section 9.3. In Section 9.4, some conclusions are drawn.

Business credit risk management is a scientific field which many academic and professional people have been working for, at least, the last three decades. Almost all financial organizations, such as banks, credit institutions, clients, etc., need this kind of information for some firms in which they have an interest of any kind. However, business credit risk management is not an easy thing because business credit risk management is a very complex and challenging task from the viewpoint of system engineering. It contains many processes, such as risk identification and prediction, modeling and control. In this complex system analysis, risk identification is no doubt an important and crucial step (Lai et al., 2006a), which directly influences the later processes of business credit risk management. This chapter only focuses on the business credit risk identification and analysis.

The main motivation of this chapter is to design a high-performance business credit risk identification system using knowledge ensemble strategy and meantime compare its performance with other existing single approaches. The rest of the chapter is organized as follows. Section 10.2 introduces the formulation process of the proposed EP-based knowledge ensemble methodology. Section 10.3 gives the research data and experiment design. The experiment results are reported in Section 10.4. Section 10.5 concludes the chapter.

The main contribution of this chapter is that a fully novel intelligentagent- based multicriteria fuzzy GDM model is proposed for the first time, for solving a financial MCDM problem, by introducing some intelligent agents as decision-makers. Compared with traditional GDM methods, our proposed multicriteria fuzzy GDM model has five distinct features. First of all, intelligent agents, instead of human experts, are used as decisionmakers (DMs), thus reducing the recognition bias of human experts in GDM. Second, the judgment is made over a set of criteria through advanced intelligent techniques, based upon the data itself. Third, like human experts, these intelligent agents can also generate different possible opinions on a specified decision problem, by suitable sampling and parameter setting. All possible opinions then become the basis for formulating fuzzy opinions for further decision-making actions. In this way, the specified decision problems are extended into a fuzzy GDM framework. Fourth, different from previous subjective methods and traditional time-consuming iterative procedures, this article proposes a fast optimization technique to integrate the fuzzy opinions and to make the aggregation of fuzzy opinions simple. Finally, the main advantage of the fuzzy aggregation process in the proposed methodology is that it can not only speed up the computational process via information fuzzification but also keep the useful information as possible by means of some specified fuzzification ways.

The rest of this chapter is organized as follows. In Section 11.2, the proposed intelligent-agent-based multicriteria fuzzy GDM methodology is described in detail. For illustration and verification purposes, Section 11.3 presents a simple numerical example to illustrate the implementation process of the proposed multicriteria fuzzy GDM model; three real-world credit datasets are used to test the effectiveness of the proposed multicriteria fuzzy GDM model. In Section 11.4, some concluding remarks are drawn.