Forensic Accounting Techniques in Detecting Frauds - Ali, Ahmed Mustafa; Khinger, Ibrahim Khilel; Subhe, Adel; Al-Orfali, Asmaa Kahtan (Journal of Ecohumanism, 2024)
Background: Fraud poses a significant risk to companies' financial stability, requiring strong detection and prevention techniques. This study explores how forensic accounting methods are crucial in combating fraud, especially due to the increasing complexity of fraudulent schemes that need a proactive and flexible strategy. Objective: This project aims to assess the effectiveness of several forensic accounting techniques in detecting fraudulent actions in financial systems. This goal arises from the pressing need to strengthen economic systems against the advanced risks presented by fraud. Methodology: The research thoroughly examines forensic accounting methods by analysing financial records, transactions, and other pertinent data from business environments. A multi-stage process includes data collecting, interviews, digital forensics, and statistical analysis. This methodology helps track fraudulent actions from suspicion to detection and confirmation. Results: The study demonstrates the substantial efficacy of forensic accounting methods, including anomaly detection, forensic auditing, and data analytics, as crucial to identifying financial irregularities. Case examples highlight the effective use of these approaches in different sectors, demonstrating their flexibility and effectiveness in detecting fraud. Examining each strategy in detail reveals its advantages and drawbacks. Conclusion: This study highlights the crucial importance of forensic accounting in reducing fraud risks, providing useful insights into the field. The results have practical value as they may be used in many sectors, providing businesses with strategic tools to protect their financial integrity. The report recommends the ongoing development of forensic accounting techniques to combat the ever-changing nature of fraud, which will lead to more research and the creation of more advanced detection methods.