Lyn C. Thomas

A survey of credit and behavioural scoring: forecasting financial risk of lending to consumers

Abstract Credit scoring and behavioural scoring are the techniques that help organisations decide whether or not to grant credit to consumers who apply to them. This article surveys the techniques used — both statistical and operational research based — to support these decisions. It also discusses the need to incorporate economic conditions into the scoring systems and the way the systems could change from estimating the probability of a consumer defaulting to estimating the profit a consumer will bring to the lending organisation — two of the major developments being attempted in the area. It points out how successful has been this under-researched area of forecasting financial risk.

Credit Scoring and Its Applications

From the Publisher: About the Author Lyn C. Thomas is a Professor of Management Science at the University of Southampton. Jonathan N. Crook is Reader in Business Economics at the University of Edinburgh. David B. Edelman is Credit Director of Royal Bank of Scotland, Edinburgh.

Recent developments in consumer credit risk assessment

Consumer credit risk assessment involves the use of risk assessment tools to manage a borrower’s account from the time of pre-screening a potential application through to the management of the account during its life and possible write-off. The riskiness of lending to a credit applicant is usually estimated using a logistic regression model though researchers have considered many other types of classifier and whilst preliminary evidence suggest support vector machines seem to be the most accurate, data quality issues may prevent these laboratory based results from being achieved in practice. The training of a classifier on a sample of accepted applicants rather than on a sample representative of the applicant population seems not to result in bias though it does result in difficulties in setting the cut off. Profit scoring is a promising line of research and the Basel 2 accord has had profound implications for the way in which credit applicants are assessed and bank policies adopted.

Survival Analysis Methods for Personal Loan Data

Credit scoring is one of the most successful applications of quantitative analysis in business. This paper shows how using survival-analysis tools from reliability and maintenance modeling allows one to build credit-scoring models that assess aspects of profit as well as default. This survival-analysis approach is also finding favor in credit-risk modeling of bond prices. The paper looks at three extensions of Coxs proportional hazards model applied to personal loan data. A new way of coarse-classifying of characteristics using survival-analysis methods is proposed. Also, a number of diagnostic methods to check adequacy of the model fit are tested for suitability with loan data. Finally, including time-by-characteristic interactions is proposed as a way of possible improvement of the models predictive power.

Benchmarking state-of-the-art classification algorithms for credit scoring: An update of research

Many years have passed since Baesens et al. published their benchmarking study of classification algorithms in credit scoring [Baesens, B., Van Gestel, T., Viaene, S., Stepanova, M., Suykens, J., & Vanthienen, J. (2003). Benchmarking state-of-the-art classification algorithms for credit scoring. Journal of the Operational Research Society, 54(6), 627–635.]. The interest in prediction methods for scorecard development is unbroken. However, there have been several advancements including novel learning methods, performance measures and techniques to reliably compare different classifiers, which the credit scoring literature does not reflect. To close these research gaps, we update the study of Baesens et al. and compare several novel classification algorithms to the state-of-the-art in credit scoring. In addition, we examine the extent to which the assessment of alternative scorecards differs across established and novel indicators of predictive accuracy. Finally, we explore whether more accurate classifiers are managerial meaningful. Our study provides valuable insight for professionals and academics in credit scoring. It helps practitioners to stay abreast of technical advancements in predictive modeling. From an academic point of view, the study provides an independent assessment of recent scoring methods and offers a new baseline to which future approaches can be compared.

A survey of the issues in consumer credit modelling research

Methods for assessing the credit risk when lending to consumers has been in operation for 50 years. Yet, there are probably now more opportunities and challenges for research into the development of this area than ever before. This paper surveys the development of the methodology, describes the current environment for consumer lending and seeks to identify some of the modelling areas and issues that are actively being researched or should be.

An aggregate stochastic dynamic programming model of multireservoir systems

We present a new method of determining an operating policy for a multireservoir system in which the operating policy for a reservoir is determined by solving a stochastic dynamic programming model consisting of that reservoir and a two-dimensional representation of the rest of the system. The method is practical for systems with many reservoirs because the time required to determine an operating policy only increases quadratically with the number of reservoirs in the system and because the operating policy for a reservoir is a function of few variables. We apply the method to examples of multireservoir systems with between 3 and 17 reservoirs and show that the operating policies determined are very close to optimal.

Sample selection bias in credit scoring models

One of the aims of credit scoring models is to predict the probability of repayment of any applicant and yet such models are usually parameterised using a sample of accepted applicants only. This may lead to biased estimates of the parameters. In this paper we examine two issues. First, we compare the classification accuracy of a model based only on accepted applicants, relative to one based on a sample of all applicants. We find only a minimal difference, given the cutoff scores for the old model used by the data supplier. Using a simulated model we examine the predictive performance of models estimated from bands of applicants, ranked by predicted creditworthiness. We find that the lower the risk band of the training sample, the less accurate the predictions for all applicants. We also find that the lower the risk band of the training sample, the greater the overestimate of the true performance of the model, when tested on a sample of applicants within the same risk band — as a financial institution would do. The overestimation may be very large. Second, we examine the predictive accuracy of a bivariate probit model with selection (BVP). This parameterises the accept–reject model allowing for (unknown) omitted variables to be correlated with those of the original good–bad model. The BVP model may improve accuracy if the loan officer has overridden a scoring rule. We find that a small improvement when using the BVP model is sometimes possible.

Should Start-up Companies Be Cautious? Inventory Policies Which Maximise Survival Probabilities

New start-up companies, which are considered to be a vital ingredient in a successful economy, have a different objective than established companies: They want to maximise their chance of long-term survival. We examine the implications for their operating decisions of this different criterion by considering an abstraction of the inventory problem faced by a start-up manufacturing company. The problem is modelled under two criteria as a Markov decision process; the characteristics of the optimal policies under the two criteria are compared. It is shown that although the start-up company should be more conservative in its component purchasing strategy than if it were a well-established company, it should not be too conservative. Nor is its strategy monotone in the amount of capital it has available. The models are extended to allow for interest on investment and inflation.

PHAB scores: proportional hazards analysis behavioural scores

Credit scoring is one of the most widely used applications of quantitative analysis in business. Behavioural scoring is a type of credit scoring that is performed on existing customers to assist lenders in decisions like increasing the balance or promoting new products. This paper shows how using survival analysis tools from reliability and maintenance modelling, specifically Coxs proportional hazards regression, allows one to build behavioural scoring models. Their performance is compared with that of logistic regression. Also the advantages of using survival analysis techniques in building scorecards are illustrated by estimating the expected profit from personal loans. This cannot be done using the existing risk behavioural systems.