Evolutionary Rule Generation and its Application to Credit Scoring

Abstract

Credit scoring is an important financial application area, concerned with assessing the likely risk of customers defaulting on granted credit. These customers may be bank customers borrowing money, or retail customers being sold goods on a deferred payment scheme. In order to model the relationship between the characteristics of a customer (financial and demographic) and their measured credit risk, data is collected and analysed. The developed model can then be used with new customers to determine their expected behaviour in an effort to automate the decision making process. The task of learning to classify objects in a database according to their attributes or characteristics has been tackled by many approaches over the decades. Statistical approaches such as logistic regression and discriminant analysis have yielded over recent years to soft computing methods such as neural networks, fuzzy logic and genetic algorithms. One of the advantages of soft computing techniques lies in their modeling capabilities in the presence of noisy, imprecise, inaccurate, or missing data. These considerations become specially important in applications such as credit scoring where the data attributes are likely to be incomplete, the classification of the customers as good or bad credit risks may be erroneous, and the costs of misclassification are particularly high.KeywordsGenetic AlgorithmFitness FunctionCredit RiskDecision AttributeFitness ComponentThese keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.