Forensic auditing is a critical component of ensuring financial integrity and detecting fraud within organizations. Traditional methods of risk assessment in forensic auditing often rely on manual processes, which can be time-consuming, labour-intensive, and prone to human error. In recent years, the integration of artificial intelligence (AI) techniques has revolutionized the field, offering more efficient and accurate risk assessment capabilities. This abstract explores the role of AI-based risk assessment in forensic auditing, highlighting its benefits, challenges, and future implications.

AI-based risk assessment leverages advanced algorithms and machine learning models to analyse large volumes of financial data, identify patterns, anomalies, and potential red flags indicative of fraudulent activities. By automating repetitive tasks such as data collection, classification, and analysis, AI streamlines the auditing process, enabling forensic auditors to focus on interpreting results and making informed decisions. One of the primary advantages of AI-based risk assessment is its ability to detect complex fraud schemes that may go unnoticed by traditional methods. Machine learning algorithms can detect subtle deviations from expected behaviour, flagging transactions or activities that exhibit unusual patterns or characteristics. Moreover, AI systems can adapt and learn from new data, continuously improving their detection capabilities over time. Another benefit of AI-based risk assessment is its scalability and efficiency. With the increasing volume and complexity of financial transactions, manual auditing processes struggle to keep pace. AI, on the other hand, can analyse vast datasets in a fraction of the time it would take a human auditor, allowing organizations to conduct more comprehensive and timely audits.

However, despite its promise, AI-based risk assessment in forensic auditing also presents several challenges. One of the primary concerns is the black-box nature of some machine learning algorithms, which makes it difficult to understand how decisions made. Ensuring transparency and interpretability in AI models is crucial for building trust and confidence in their findings.

Furthermore, the quality of AI-based risk assessment depends heavily on the availability and quality of data. Biases and inaccuracies in the training data can lead to erroneous conclusions and false positives/negatives. Therefore, careful consideration given to data selection, Preprocessing, and validation to ensure the reliability and robustness of AI-driven audit processes. Looking ahead, the future of AI-based risk assessment in forensic auditing is promising. As AI technologies continue to evolve, we can expect further advancements in detection accuracy, efficiency, and interpretability. Additionally, the integration of emerging technologies such as blockchain and Natural Language Processing (NLP) could enhance the capabilities of AI systems, enabling more sophisticated fraud detection techniques. In conclusion, AI-based risk assessment holds tremendous potential for transforming forensic auditing by offering enhanced detection capabilities, scalability, and efficiency. However, addressing challenges related to transparency, data quality, and interpretability is essential for realizing the full benefits of AI in forensic auditing practice. Continued research, innovation, and collaboration between technology experts and forensic auditors are crucial for advancing the field and staying ahead of increasingly sophisticated fraudulent activities.

Keywords: Forensic Auditing, Risk Assessment, ML Algorithms, Scability and Efficiency, Black-Box nature of ML Algorithms, Transparency and Interpretability, Natural Language Processing (NLP)