Prediction analysis of risky credit using Data mining classification models

Abstract

Gaining as many good credit scores are beneficial for customers in numerous ways and it also allows banks to analyse their clients and to give credit loans to them accordingly. In this paper, we look whether data mining techniques are useful to predict and classify the customer's credit score (good/bad) to overcome the future risks giving loans to clients who cannot repay. We use historical given dataset of a bank for our predictive modelling (general models), banks can use them for the better outcome of their overall credit system. For example, if a customer is assigned a bad credit score after applying these predictive classification models, then the bank will not allow giving that customer a future credit and will quickly analyse all the other risky credits.