Big Data Audit of Banks Based on Fuzzy Set Theory to Evaluate Risk Level

Abstract

The arrival of big data era has brought new opportunities and challenges to the development of various industries in China. The explosive growth of commercial bank data has brought great pressure on internal audit. The key audit of key products limited to key business areas can no longer meet the needs. It is difficult to find abnormal and exceptional risks only by sampling analysis and static analysis. Exploring the organic integration and business processing methods between big data and bank internal audit, Internal audit work can protect the stable and sustainable development of banks under the new situation. Therefore, based on fuzzy set theory, this paper determines the membership degree of audit data through membership function, and judges the risk level of audit data, and builds a risk level evaluation system. The main features of this paper are as follows. First, it analyzes the necessity of transformation of the bank auditing in the big data environment. The second is to combine the determination of the membership function in the fuzzy set theory with the bank audit analysis, and use the model to calculate the corresponding parameters, thus establishing a risk level assessment system. The third is to propose audit risk assessment recommendations, hoping to help bank audit risk management in the big data environment. There are some shortcomings in this paper. First, the amount of data acquired is not large enough. Second, due to the lack of author’ knowledge, there are still some deficiencies in the analysis of audit risk of commercial banks.