Enhancing Fraud Detection in Financial Statements with Deep Learning: An Audit Perspective

Abstract

Financial fraud persists as a formidable challenge, necessitating continuous audit innovations to uphold financial statement integrity. This study explores the application of Deep Learning (DL) techniques in bolstering fraud detection within financial audits. It emphasizes the pivotal role of audits in preserving trust and transparency in business. Highlighting the evolving nature of financial fraud, the study underscores the need for auditors to adapt to sophisticated schemes. An examination of DL methodologies reveals the potential of neural networks, anomaly detection, and predictive modeling in uncovering hidden fraudulent activities. The discourse encompasses data-driven strategies, model architectures, and tailored feature engineering. Real-world case studies demonstrate how DL-driven fraud detection enhances traditional methods by improving accuracy and reducing false positives. The study stresses the importance of continuous monitoring, proactive risk mitigation, and timely fraud prevention. Additionally, it addresses ethical and regulatory considerations, advocating for transparency and responsible AI practices in auditing. In conclusion, this study serves as a valuable resource for auditors and regulators, highlighting the transformative impact of DL in fortifying fraud detection and preserving financial reporting integrity.