Optimized algorithm for Credit Scoring

Abstract

The rapid expansion of credit scoring technologies is increased today. Credit scoring will be considered as the significant element in the financial industries. It plays an important role in modern affairs such as credit customer selection, risk measurement, post-loan and after-loan supervision, comprehensive performance evaluation etc. Credit scoring has been recognized as a binary classification technique distinguishing applicants into two classes: good credit and bad credit, based on characteristics such as gender, age, occupation, and salary. These determine the applicability of loans for applicants. There are two main stream classification techniques, statistical techniques and machine learning techniques. Linear discriminant analysis and logistic regression are the two most commonly used statistical techniques in credit scoring. Machine learning techniques include K-nearest neighbor, support vector machine, decision tree and neural network. Use different best algorithms for classify the credit scoring data sets. Here uses four algorithms for the classification of credit scoring data sets and then the accuracy of different algorithms on the data sets will be obtained.