Decision-Oriented Framework on Detecting and Countering Finance Management Fraud Amid Big Data Times: Characteristics, Risk Assessment and Technology-Driven Countermeasures

Yilin Lv; Youyou Shang; Qianhui Yu; Tianyue Zhao; Shuti Li

doi:10.31181/dmame8220251574

Authors

Yilin Lv Guangzhou College of Technology and Business
Youyou Shang Ruthin school
Qianhui Yu Chase Grammar School
Tianyue Zhao SSAL College
Shuti Li Ruthin school

DOI:

https://doi.org/10.31181/dmame8220251574

Keywords:

Data Control Technology, Financial Fraud, AI Fraud Detection, Decision-Oriented Framework, Internal Audit

Abstract

The objective of this study was to examine how data governance maturity, artificial intelligence (AI) integration, internal audit frequency, employee training, access control robustness, and the implementation of anomaly detection systems affect the occurrence of financial fraud. The investigation focused on business organisations operating in China. The dataset consisted of 218 organisations, with each organisation serving as the unit of analysis. Data analysis was performed using RStudio with R programming, applying exploratory factor analysis and regression modelling techniques to the organisational data. The findings indicate that AI integration, employee training, and access control robustness have no significant effect on financial fraud occurrence. In contrast, data governance maturity, internal audit frequency, and the application of anomaly detection systems were found to significantly influence the incidence of financial fraud within business organisations. These results provide valuable insights for Chinese businesses aiming to reduce the likelihood of financial fraud.

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References

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