International Journal For Multidisciplinary Research

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

Author(s) Intissar Grissa, Ezzeddine Abaoub
Country Tunisia
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.
Keywords Deep Learning, Fraud detection, Financial statements, Audit perspective, Anomaly detection, Regulatory compliance, Responsible AI.
Field Business Administration
Published In Volume 6, Issue 1, January-February 2024
Published On 2024-02-28
Cite This Enhancing Fraud Detection in Financial Statements with Deep Learning: An Audit Perspective - Intissar Grissa, Ezzeddine Abaoub - IJFMR Volume 6, Issue 1, January-February 2024. DOI 10.36948/ijfmr.2024.v06i01.13451
DOI https://doi.org/10.36948/ijfmr.2024.v06i01.13451
Short DOI https://doi.org/gtktjr
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