Applying Deep Learning to Detect Revenue Recognition Fraud

Abstract

Financial fraud, especially in revenue recognition, poses significant challenges to modern financial systems, directly impacting transparency and public trust in financial markets. Traditional fraud detection methods have limited effectiveness in analyzing complex and large datasets. Deep learning, as an advanced artificial intelligence technology, demonstrates strong capabilities in detecting hidden and unusual patterns in financial data. This study aims to explore the application of deep learning in detecting revenue recognition fraud and compare its effectiveness with traditional methods. This descriptive-analytical study reviews the role of deep learning in detecting financial fraud. Data used include scientific articles and previous studies related to deep learning in the financial domain. Data analysis was conducted using qualitative methods, comparing the performance of deep learning with traditional techniques. Deep learning has shown superior performance in detecting complex and sophisticated fraud compared to traditional methods. It has achieved higher accuracy in identifying suspicious transactions and reducing false alarms. Additionally, deep learning has the capability to predict future fraud and uncover hidden fraud patterns, and it outperforms traditional methods in analyzing multidimensional financial data. Deep learning is recognized as an effective tool for detecting financial fraud and can enhance financial transparency by improving accuracy and efficiency. However, challenges such as the need for large datasets and ethical concerns related to AI use in this field must be addressed.