Recalibrating scorecards

Many credit scoring systems depend on scorecards which order applicants by credit risk. However the scorecards may also have other properties with certain scores reflecting certain good:bad odds or differences in scores having the same property throughout the score range. Other properties like positivity of attribute points may be required for palatability or internal marketing reasons. The paper outlines the results of a small survey of what properties scorecard builders require of their scorecards. It then discusses how these properties can be obtained and describes a linear programming formulation which recalibrates scorecards so as to produce the best approximate scorecard with the properties required.     

Selection bias in credit scorecard evaluation

Selection bias is a perennial problem when constructing and evaluating scorecards. It is familiar in the context of reject inference, but crops up in many other situations as well. In this paper, we examine the impact of how accepting or rejecting customers using one scorecard leads to biased comparisons of performance between that scorecard and others. This has important implications for organisations seeking to improve or replace scorecards.     

Kalman filtering as a performance monitoring technique for a propensity scorecard

Propensity scorecards allow forecasting, which bank customers would like to be granted new credits in the near future, through assessing their willingness to apply for new loans. Kalman filtering can help to monitor scorecard performance. Data from successive months are used to update the baseline model. The updated scorecard is the output of the Kalman filter. There is no assumption concerning the scoring model specification and no specific estimation method is presupposed. Thus, the estimator covariance is derived from the bootstrap. The focus is on a relationship between the score and the natural logarithm of the odds for that score, which is used to determine a customer's propensity level. The propensity levels corresponding to the baseline and updated scores are compared. That comparison allows for monitoring whether the scorecard is still up-to-date in terms of assigning the odds. The presented technique is illustrated with an example of a propensity scorecard developed on the basis of credit bureau data.     

Scoring decisions in the context of economic uncertainty

We consider methods for incorporating forecasts of future economic conditions into acquisition decisions for scored retail credit and loan portfolios. We suppose that a portfolio manager is faced with two possible future economic scenarios, each characterised by a known probability of occurrence and by known performance functions that give expected profit and volume. We suppose further that he must choose in advance the scoring strategy and score cutoffs to optimise performance. We show that, despite the uncertainty of performance induced by economic conditions, every efficient policy consists of a single cutoff, provided the expected profit and volume performance curves in each scenario are concave. If these curves are not concave, efficient operating points can be characterised as cutoffs on a redefined score. In cases in which two scorecards are available, we show that it may be advantageous to randomly choose the scorecard to be employed, and we provide methods for selecting efficient operating points. Discussion is limited to cases with two scorecards and two economic scenarios, but our approach and results generalise to more scorecards and more economic scenarios.     

A dynamic scorecard for monitoring baseline performance with application to tracking a mortgage portfolio

A principled technique for monitoring the performance of a consumer credit scorecard through time is derived from Kalman filtering. Standard approaches sporadically compare certain characteristics of the new applicants with those predicted from the scorecard. The new approach systematically updates the scorecard combining new applicant information with the previous best estimate. The dynamically updated scorecard is tracked through time and compared to limits calculated by sequential simulation from the baseline scorecard. The observation equation of the Kalman filter is tailored to take the results of fitting local scorecards by logistic regression to batches of new clients that arrive in the current time interval. The states in the Kalman filter represent the true or underlying score for each attribute in the card: the parameters of the logistic regression. Their progress in time is modelled by a random walk and the filter provides the best estimate of the scores using past and present information. We illustrate the technique using a commercial mortgage portfolio and the results indicate significant emerging deficiencies in the baseline scorecard.     

Time varying or static cut-offs for credit scorecards

This note points out that the ability of credit scorecards to separate Goods from Bads changes over time. A simple way of dealing with such changes is to adjust the cut-off scores being used. These adjustments can be made by using the score to log odds relationship which is regularly monitored. In a case study there are decreases in the costs—in some case considerable decreases—of using the scorecard by making such adjustments compared with making no adjustments.     

Optimal scoring cutoff policies and efficient frontiers

Multiple business objectives are increasingly important in determining account acquisition and management policies in scored retail credit and loan portfolios. These business objectives include profit and market share, as well as the more traditional management of risk. We formulate a mathematical model that addresses the problem of how acquisition decisions should be made with multiple, conflicting objectives when one, or more than one, scorecard is available to the portfolio manager. We show that iso-contours for expected profit, volume and loss are straight lines in the receiver operating characteristic (ROC) space and develop results that establish equivalence between ROC dominance, maximum expected profit, and efficient-frontier dominance in the space of multiple business measures. For two non-dominating scorecards, we derive the efficient frontiers in the profit-volume space and provide guidelines for choosing optimal policies based on the decision maker's trade-offs between objectives.     

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 Cox's 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.     

The benefit to consumers from generic scoring models based on credit reports

Received on 1 July 1991. The benefit to consumers from the use of informative creditreports is demonstrated by showing the improvement in creditdecisions when generic scoring models based on credit reportsare implemented. If these models are highly predictive, thenthe truncation of credit reports will reduce the predictivepower of bureau-based generic scoring systems. As a result,more good credit risks will be denied credit, and more poorcredit risks will be granted credit. It is shown that, evenwhen applied to credit applications that had already been screenedand approved, the use of generic scoring models significantlyimproves credit grantors' ability to predict and eliminate bankruptcies,charge-offs, and delinquencies. As applied to existing accounts,bureau-based generic scores are shown to have predictive valuefor at least 3 months, while scores 12 months old may not bevery powerful. Even though bureau-based scores shift towardsthe high-risk end of the distribution during a recession, theycontinue to rank risk very well. When coupled with application-basedcredit-scoring models, scores based on credit-bureau data furtherimprove the predictive power of the model-the improvements beinggreater with more complete bureau information. We conclude thatgovernment-imposed limits on credit information are anti-consumerby fostering more errors in credit decisions.     

Technology credit rating system for funding SMEs

Technology evaluation has played a crucial role in selecting and supporting companies with innovative technology. Previous studies have focused on developing technology evaluation methods such as scorecard. However, technology credit rating is rarely applied, despite its convenient usage for technology financing. In this paper, we propose a technology credit rating system, called cross matrix, based on empirical data obtained from the technology scoring model and examine their properties. The proposed rating system is expected to provide valuable information for effective management of the technology credit fund.     

Scoring by usage

This paper aims to discover whether the predictive accuracy of a new applicant scoring model for a credit card can be improved by estimating separate scoring models for applicants who are predicted to have high or low usage of the card. Two models are estimated. First we estimate a model to explain the desired usage of a card, and second we estimate separately two further scoring models, one for those applicants whose usage is predicted to be high, and one for those for whom it is predicted to be low. The desired usage model is a two-stage Heckman model to take into account the fact that the observed usage of accepted applicants is constrained by their credit limit. Thus a model of the determinants of the credit limit, and one of usage, are both estimated using Heckman's ML estimator. We find a large number of variables to be correlated with desired usage. We also find that the two stage scoring methodology gives only very marginal improvements over a single stage scoring model, that we are able to predict a greater percentage of bad payers for low users than for high users and a greater percentage of good payers for high users than for low users.     

Lookahead scorecards for new fixed term credit products

Standard approaches to scorecard construction require that a body of data has already been collected for which the customers have known good/bad outcomes, so that scorecards can be built using this information. This requirement is not satisfied by new financial products. To overcome this lack, we describe a class of models based on using information about the length of time customers have been using the product, as well as any available information which does exist about true good/bad outcome classes. These models not only predict the probability that a new customer will go bad at some time during the product's term, but also evolve as new information becomes available. Particular choices of functional form in such models can lead to scorecards with very simple structures. The models are illustrated on a data set relating to loans.     

Enterprise risk management: coping with model risk in a large bank

Enterprise risk management (ERM) has become an important topic in today's more complex, interrelated global business environment, replete with threats from natural, political, economic, and technical sources. Banks especially face financial risks, as the news makes ever more apparent in 2008. This paper demonstrates support to risk management through validation of predictive scorecards for a large bank. The bank developed a model to assess account creditworthiness. The model is validated and compared to credit bureau scores. Alternative methods of risk measurement are compared.     

A comparison of strategies to develop a customer default scoring model

Behavioural scoring models are generally used to estimate the probability that a customer of a financial institution who owns a credit product will default on this product in a fixed time horizon. However, one single customer usually purchases many credit products from an institution while behavioural scoring models generally treat each of these products independently. In order to make credit risk management easier and more efficient, it is interesting to develop customer default scoring models. These models estimate the probability that a customer of a certain financial institution will have credit issues with at least one product in a fixed time horizon. In this study, three strategies to develop customer default scoring models are described. One of the strategies is regularly utilized by financial institutions and the other two will be proposed herein. The performance of these strategies is compared by means of an actual data bank supplied by a financial institution and a Monte Carlo simulation study.     

A literature review on the application of evolutionary computing to credit scoring

The last years have seen the development of many credit scoring models for assessing the creditworthiness of loan applicants. Traditional credit scoring methodology has involved the use of statistical and mathematical programming techniques such as discriminant analysis, linear and logistic regression, linear and quadratic programming, or decision trees. However, the importance of credit grant decisions for financial institutions has caused growing interest in using a variety of computational intelligence techniques. This paper concentrates on evolutionary computing, which is viewed as one of the most promising paradigms of computational intelligence. Taking into account the synergistic relationship between the communities of Economics and Computer Science, the aim of this paper is to summarize the most recent developments in the application of evolutionary algorithms to credit scoring by means of a thorough review of scientific articles published during the period 2000–2012.     

Identifying future defaulters: A hierarchical Bayesian method

Traditional methods of applying classification models into the area of credit scoring may ignore the effect from censoring. Survival analysis has been introduced with its ability to deal with censored data. The mixture cure model, one important branch of survival models, is also applied in the context of credit scoring, assuming that the study population is a mixture of never-default and will-default customers.     

Adaptive credit scoring with kernel learning methods

Credit scoring is a method of modelling potential risk of credit applications. Traditionally, logistic regression and discriminant analysis are the most widely used approaches to create scoring models in the industry. However, these methods are associated with quite a few limitations, such as being instable with high-dimensional data and small sample size, intensive variable selection effort and incapability of efficiently handling non-linear features. Most importantly, based on these algorithms, it is difficult to automate the modelling process and when population changes occur, the static models usually fail to adapt and may need to be rebuilt from scratch. In the last few years, the kernel learning approach has been investigated to solve these problems. However, the existing applications of this type of methods (in particular the SVM) in credit scoring have all focused on the batch model and did not address the important problem of how to update the scoring model on-line. This paper presents a novel and practical adaptive scoring system based on an incremental kernel method. With this approach, the scoring model is adjusted according to an on-line update procedure that can always converge to the optimal solution without information loss or running into numerical difficulties. Non-linear features in the data are automatically included in the model through a kernel transformation. This approach does not require any variable reduction effort and is also robust for scoring data with a large number of attributes and highly unbalanced class distributions. Moreover, a new potential kernel function is introduced to further improve the predictive performance of the scoring model and a kernel attribute ranking technique is used that adds transparency in the final model. Experimental studies using real world data sets have demonstrated the effectiveness of the proposed method.     

A note on coarse classifying in acceptance scorecards

Traditionally, in credit and behavioural scoring one assumes that as all consumers have essentially the same product, its features will not affect whether the consumer defaults or not. Hence, one coarse classifies the characteristics concentrating only on the default ratio. As products and their operational features become customized for each individual (the very purpose of acceptance scoring), then decisions like whether the customer will accept the product or not must depend on the features offered. This paper investigates how one can deal with this dependency when coarse classifying the characteristics.     

Technology credit scoring model considering both SME characteristics and economic conditions: The Korean case

In order to support small and medium enterprises (SME) with a high degree of growth potential in technology, various kinds of technology credit guarantees are issued to companies that obtain high scores by a technology scorecard in Korea. However, their default rates are reported to be very high. The main goal of this study is to propose a new technology evaluation model that accommodates not only technology-related attributes but also environmental conditions such as firm-specific characteristics and economic situations in the manner of more objective. We then show the superior prediction ability of the proposed model to the existing one. This model also enables to apply to a stress test by considering some worst environmental situations and is expected to be used for the effective management of the various technology funds for SMEs.     

电网项目融资租赁信用评价混合模型的新研究

电网建设工程通过项目融资租赁进行快速融资的同时,给租赁公司带来巨大的信用风险.通过事前对承租人进行信用评价,能够有效降低信用风险损失.针对电网企业信用评价的多属性非线性特征,提出了基于独立分量分析技术-支持向量机的信用评价混合模型.首先,采用独立分量分析技术对信用属性数据进行属性重构,实现属性数据的去噪.然后,将重构后的新信用属性数据用于支持向量机的训练建模.最后,通过实例模拟对比分析了独立分量分析技术对支持向量机分类的有效性.结果表明,独立分量分析技术能够改善信用属性数据特征,并且在多属性分类问题中,独立分量分析技术有助于提高支持向量机分类的准确率.