Credit Score Prediction System using Deep Learning and K-Means Algorithms

In financial markets, credit rating and risk assessment tools are used to minimize potential risk up to some extent for credit score. Nowadays, the banking and financial industry has experienced rapid expansion. Therefore, with this growth, the numbers of credit card applications with various credit products are increasing day by day because many people want to avail these services for their personal interest. The challenge here is to identify insights on the performance of a finance industry by using deep learning algorithms as they directly affect the viability of that industry. These industries have a limited number of resources and capital, which can be used to deliver the services among the customers. In this research work, we proposed prediction of credit scoring system using deep learning and K-Means algorithm for the financial industry. The scheme contains a predictive model which uses feature selection (FS) classification and deep learning applications simultaneously to train the proposed model to perform effectively. The scheme 1) pre-processing credit card data 2) uses a feature selection technique to minimize the dimension of data in order to obtain the finest training data 3) applies a deep learning algorithm to map the input weight with hidden biases to achieve excellent performance 4) Decision support system is used to enable the deep learning algorithm to provide a more accurate and intelligent decision. Furthermore, the proposed model is validated on different credit scoring dataset in real-world scenarios and is capable of improving the effectiveness and accuracy. The studies indicate that our predictive model performs well for credit scoring of existing customer and helps lenders to allocate funds in finance industry.