Improve profiling bank customer's behavior using machine learning

Abstract

In the banking industry, credit card evolution is a noticeable occurrence. Each banking system includes a huge dataset for customer's transactions of their credit cards. Therefore, banks would be in need of customer profiling. Profiling bank customer's cognize the issuer's decisions about whom to give banking facilities and what a credit limit to provide. It also helps the issuers get a better understanding of their potential and current customers. In previous research, Customer profiling mainly depends on transaction data or demographic data, but in this research, we merge both data in order to get a more accurate result and minimize the risk. By finding the best technique, it leads to improvement in accuracy and helps banks to get higher profitability by customer satisfaction through a focus on the valuable customer (companies) which consider as the main engine in the bank's profitability. This study aims at using k-mean, improved k-mean, fuzzy c-means and neural networks. The used dataset is labeled and creating a ýnew label as a target for neural network classification is the main aspect of this study, which helps to reduce the clustering execution time and get the best accuracy results. Finally, by comparing the accuracy ratio it shows that the neural network ýis the best clustering technique.