Research on Credit Risk Control of Commercial Banks Based on Data Mining Technology

Abstract

Credit risk is the most important part of commercial banks' risk management, risk management is the core issue of banks' financial management, and the good or bad credit risk management directly affects banks' efficiency and is one of the effective means to avoid non-performing loans. Under the various business development and promotion of banks, their non-performing loan rate is also growing year by year, which not only affects the rapid and healthy development of banks, but even leads to the expansion of the scale of bank liabilities. In the era of big data, the importance of proper utilization of data has gradually emerged, which brings challenges and opportunities to commercial banks, and the ability to make good use of big data has become the key to risk management for commercial banks. Data mining is a cross-disciplinary field that brings together techniques and methods from databases, statistics, machine learning and other fields. It can uncover potentially useful information and knowledge from a large amount of banking data and provide managers with effective information for decision making, which in turn can prevent and manage risks more effectively. Based on data mining technology, this paper investigates bank credit risk management methods to effectively solve the credit risk control problems of commercial banks, optimize the credit project process, improve the quality of credit delivery, and provide reference for credit risk management related personnel.