Credit Scoring Model using Data Mining Algorithms

Computer Engineering

Credit scoring means applying a statistical model to assign a risk score to a credit application.Credit scoring techniques assess the risk in lending to a particular client.They not only identify good applications and bad applications on an individual basis, but also they forecast the probability that an applicant with any given score will be good or bad.Although credit scoring systems are being implemented and used by most banks nowadays, they do face a number of limitations.The availability of highquality data is a very important prerequisite for building good credit scoring models. However, the data need not only be of high quality, but it should be predictive as well, in the sense that the captured characteristics are related to the customer defaulting or not.The statistical techniques used in developing credit scoring models typically assume a data set of sufficient size containing enough details. This may not always be the case for specific types of portfolios where only limited data is available, or only a low number of defaults is observed. It is observed that Credit Scoring Model is much more accurate and efficient when it is executed on data that has been carefully prepared and pre-processed. Data mining could be applied in the process of Credit Scoring that is used to predict default clients in order to decide whether to grant them a credit especially by using classification algorithms. Also, data pre-processing can be used on imbalance credit data for improving risk prediction.

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