@Article{jbd.2020.01002,
AUTHOR = {Yilin Bi, Yuxin Ouyang, Guang Sun, Peng Guo, Jianjun Zhang, Yijun Ai},
TITLE = {Big Data Audit of Banks Based on Fuzzy Set Theory to Evaluate  Risk Level},
JOURNAL = {Journal on Big Data},
VOLUME = {2},
YEAR = {2020},
NUMBER = {1},
PAGES = {9--18},
URL = {http://www.techscience.com/jbd/v2n1/40127},
ISSN = {2579-0056},
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.},
DOI = {10.32604/jbd.2020.01002}
}