Machine Learning for Anomaly Detection in Accounting Records: A Comprehensive Study

Abstract

Maintaining the integrity of accounting records is vital for financial transparency, regulatory compliance, and fraud prevention. This study explores the application of Machine Learning (ML) techniques for anomaly detection in accounting records, shedding light on the evolving landscape of financial data analysis. It emphasizes the critical role of accurate accounting records in business operations, financial reporting, and regulatory adherence. Amidst increasing transaction complexity, traditional audit methods face challenges in detecting unnoticed irregularities. A detailed examination of ML methodologies, including clustering, classification, and neural networks, showcases their potential in identifying anomalies within accounting data. The study discusses data preprocessing, feature engineering, model selection, and evaluation criteria essential for robust anomaly detection. Real-world case studies illustrate how ML-driven anomaly detection enhances traditional accounting practices, improving accuracy and efficiency. It underscores ML's proactive role in preventing fraud, errors, and compliance breaches. Ethical and regulatory considerations in ML implementation are addressed, highlighting the importance of transparency, accountability, and responsible AI practices. This study serves as a valuable resource for accounting professionals and regulatory authorities, emphasizing ML's transformative impact on maintaining financial accuracy and regulatory compliance.