A Novel Algorithm Developed with Integrated Metrics for Dynamic and Smart Credit Rating of Bank Customers

Abstract

There are a wide variety of algorithms for bank customer credit rating. Over-allocation or under-allocation of credit arises from weakness in algorithms and lack of software programs involving efficient metrics. This in turn gives rise to legal and criminal issues between banks and customers, poor utilization of customer capabilities, and inappropriate provision of banking services. This study intended to propose qualitative metrics to identify the best customer credit rating model with a focus on financial transitions. Instead of focusing on customer credit, this study employed a concept known as discredit derived from the concepts concerning system quality assurance. The new model was validated through efficiently developed software including metric information and customer data. Over the past four years, the account information about 56,000 customers of an international bank branch was studied to determine the criteria and metrics of their credits using different modeling techniques. The developed software was used to define, analyze, and statistically test multiple financial metrics for the financial information of an international bank branch, while fitting the best metrics in a dynamic model for discredit detection. The best coefficients for combination of financial metric were calculated by weighting based on time, while extracting and validating appropriate equations for the newly proposed model. More specifically, the current year account balance was correlated with discredit, whereas the previous year account balances were not correlated. In addition, the discredit data involved a somewhat greater regression than the numerical discredit data.