Utilization of Artificial Intelligence for Automating Investigative Audits in Banking Transaction Data
DOI:
https://doi.org/10.70062/jiafc.v1i3.270Keywords:
Anomaly, Artificial Intelligence, Banking, Efficiency, Fraud DetectionAbstract
Background: The rise of artificial intelligence (AI) has had a significant impact on various sectors, particularly banking, where AI promises to enhance the efficiency and accuracy of audits. Traditional auditing methods often struggle to detect fraudulent transactions due to the increasing complexity and volume of financial data. With financial institutions handling vast amounts of real-time transaction data, the ability to identify anomalies promptly and accurately becomes critical. Objective: This study aims to assess the role of AI in improving banking audits, focusing on its ability to detect fraudulent activities, enhance transaction monitoring, and optimize the overall audit process. Methods: A quantitative research approach was adopted, using experimental validation of AI models applied to real banking transaction datasets. These datasets comprised both normal and anomalous transactions. Various machine learning algorithms, including decision trees, random forests, and neural networks, were employed to train AI models for detecting fraud patterns. The effectiveness of the models was measured through key performance indicators, such as accuracy, precision, recall, and time efficiency. Results: The study revealed that AI models outperformed traditional manual auditing methods. The AI-driven models achieved a 92% accuracy rate in detecting fraud, while reducing audit time by over 50%. Additionally, AI's ability to process large volumes of data in real time led to faster fraud detection and minimized false positives. The findings also indicated that AI could automate routine auditing tasks, enabling auditors to focus on more complex investigative work. This demonstrates the transformative potential of AI in revolutionizing banking audits, providing faster, more accurate, and reliable fraud detection.
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