法务会计
分析
会计
数据科学
法医学
业务
计算机科学
地理
审计
考古
作者
Amra Kapo,Lejla Turulja,Zorana Vidačak
出处
期刊:Journal of forensic accounting profession
[De Gruyter]
日期:2024-06-01
卷期号:4 (1): 1-14
标识
DOI:10.2478/jfap-2024-0001
摘要
Abstract The integration of data analytics into forensic accounting has revolutionized the detection and prevention of financial fraud. This paper conducts a comprehensive analysis of recent advancements in this field, highlighting the application of machine learning, data mining, and big data techniques in identifying fraudulent activities. By reviewing the latest research and examining case studies, we demonstrate the enhanced accuracy and efficiency these technologies offer over traditional methods. The findings suggest that financial institutions should adopt these advanced tools to mitigate fraud risks and improve overall financial security. The paper also explores future research directions, emphasizing the need for developing hybrid models and real-time detection systems to further enhance fraud detection capabilities.
科研通智能强力驱动
Strongly Powered by AbleSci AI