Personal Credit Score Prediction using Data Mining Algorithms (Case Study: Bank Customers)

Knowledge and information extraction from data is an age-old concept in scientific studies. In industrial decision-making processes, the application of this concept gives rise to data-mining opportunities. Personal credit scoring is an ever-vital tool for banking systems in order to manage and minimize the inherent risks of the financial sector, thus, the design and improvement of credit scoring systems based on the data-driven and machine learning techniques have garnered newfound research interest on the subject in recent years. In the present study, important variables and parameters for credit score are identified and consequently, prediction of credit score for clients of a bank is performed. CRISP-DM is employed as the reference model for the data mining process and data modelling is accomplished based on a variety of algorithms (K-nearest-neighbors, Decision tree and Random forest). Comparative results of accuracy and sensitivity of algorithms demonstrated that the k-nearest neighbour algorithm by the accuracy of 90.3% for the training set and 76.7% for test data performs suitably to predict credit score.