Would credit scoring work for Islamic finance? A neural network approach

Purpose – The main aim of this paper is to distinguish whether the decision making process of the Islamic financial houses in the UK can be improved through the use of credit scoring modeling techniques as opposed to the currently used judgmental approaches. Subsidiary aims are to identify how scoring models can reclassify accepted applicants who later are considered as having bad credit and how many of the rejected applicants are later considered as having good credit; and highlight significant variables that are crucial in terms of accepting and rejecting applicants which can further aid the decision making process. Design/methodology/approach – A real data-set of 487 applicants are used consisting of 336 accepted credit applications and 151 rejected credit applications make to an Islamic finance house in the UK. In order to build the proposed scoring models, the data-set is divided into training and hold-out sub-set. The training sub-set is used to build the scoring models and the hold-out sub-set is used to test the predictive capabilities of the scoring models.70 percent of the overall applicants will be used for the training sub-set and 30 percent will be used for the testing sub-set. Three statistical modeling techniques namely Discriminant Analysis (DA), Logistic Regression (LR) and Multi-layer Perceptron (MP) neural network are used to build the proposed scoring models. Findings – Our findings reveal that the LR model has the highest Correct Classification (CC) rate in the training sub-set whereas MP outperforms other techniques and has the highest CC rate in the hold-out sub-set. MP also outperforms other techniques in terms of predicting the rejected credit applications and has the lowest Misclassification Cost (MC) above other techniques. In addition, results from MP models show that monthly expenses, age and marital status are identified as the key factors affecting the decision making process. Research limitations/implications – Although our sample is small and restricted to an Islamic Finance house in the UK the results are robust. Future research could consider enlarging the sample in the UK and also internationally allowing for cultural differences to be identified. The results indicate that the scoring models can be of great benefit to Islamic finance houses in regards to their decision making processes of accepting and rejecting new credit applications and thus improve their efficiency and effectiveness. Originality/value –Our contribution is the first to apply credit scoring modeling techniques in Islamic Finance. Also in building a scoring model our application applies a different approach by using accepted and rejected credit applications instead of good and bad credit histories. This identifies opportunity costs of misclassifying credit applications as rejected

Consumer finance: challenges for operational research

Consumer finance has become one of the most important areas of banking, both because of the amount of money being lent and the impact of such credit on global economy and the realisation that the credit crunch of 2008 was partly due to incorrect modelling of the risks in such lending. This paper reviews the development of credit scoring—the way of assessing risk in consumer finance—and what is meant by a credit score. It then outlines 10 challenges for Operational Research to support modelling in consumer finance. Some of these involve developing more robust risk assessment systems, whereas others are to expand the use of such modelling to deal with the current objectives of lenders and the new decisions they have to make in consumer finance. <br/

Support Vector Machines for Credit Scoring and discovery of significant features

The assessment of risk of default on credit is important for financial institutions. Logistic regression and discriminant analysis are techniques traditionally used in credit scoring for determining likelihood to default based on consumer application and credit reference agency data. We test support vector machines against these traditional methods on a large credit card database. We find that they are competitive and can be used as the basis of a feature selection method to discover those features that are most significant in determining risk of default. 1

Using Memory-Based Reasoning For Predicting Default Rates On Consumer Loans

In recent years, financial institutions have struggled with high default rates for consumer lending. An ability to reliably predict the probability of consumer loan defaults would have a significant impact of the profitability of that lending for these institutions. In response to this need, the financial institutions have employed loan analysis techniques such as logistic regression, discriminant analysis, and various machine learning techniques to improve the accuracy of detecting loan defaults.  The objective of these techniques is to more precisely identify creditworthy applicants who are granted credit, thereby increasing profits, from non-creditworthy applicants who would be then denied credit, thus decreasing losses. The objective of this article is to employ an emergent data analysis technique, memory-based or case-based reasoning method, to this problem to test its accuracy in discriminating between good and bad loans. This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution considered to be qualified customers.  The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon. The paper then compares the performance of this technique to other data mining techniques proposed in earlier works and analyzes the risk of default inherent in each loan for each technique

Rule Induction Methods For Credit Scoring

Credit scoring is the term used by the credit industry to describe methods used for classifying applicants for credit into risk classes according to their likely repayment behavior (e.g. “default” and “non-default”).  The credit industry has been using such methods as logistic regression, discriminant analysis, and various machine learning techniques to more precisely identify creditworthy applicants who are granted credit, and non-creditworthy applicants who are denied credit.  Accurate classification is of benefit both to the creditor (in terms of increased profit or reduced loss) and to the loan applicant (avoiding overcommitment).  This paper examines historical data from consumer loans issued by a financial institution to individuals that the financial institution deemed to be qualified customers.  The data set consists of the financial attributes of each customer and includes a mixture of loans that the customers paid off or defaulted upon.  The paper uses rule induction methods (decision trees) to predict whether a particular applicant paid off or defaulted upon his/her loan.  The main advantage of decision trees is their ability to generate if-then classification rules which are intuitive and easy to understand. Rules could be explained to business managers who would need to approve their implementation as well as loan applicants as the reason for denying a loan.  The paper compares the correct classification accuracy rates of several decision tree algorithms with other data mining methods proposed in earlier works

Soft computing techniques applied to finance

Soft computing is progressively gaining presence in the financial world. The number of real and potential applications is very large and, accordingly, so is the presence of applied research papers in the literature. The aim of this paper is both to present relevant application areas, and to serve as an introduction to the subject. This paper provides arguments that justify the growing interest in these techniques among the financial community and introduces domains of application such as stock and currency market prediction, trading, portfolio management, credit scoring or financial distress prediction areas.Publicad