AI/ML-Powered Fraud Detection in Banking: Navigating Data Integrity Challenges

Abstract

The rise of Artificial Intelligence and Machine Learning (AI/ML) has revolutionized fraud detection in the banking sector, enabling swift and accurate identification of fraudulent activities. However, the effectiveness of AI/ML-powered fraud detection hinges on the integrity of the data driving these systems. This paper examines the landscape of AI/ML-powered fraud detection in banking and delves into the data integrity challenges that can undermine its efficacy. By analyzing data inaccuracies, biases, and regulatory compliance issues, this study provides insights into navigating these challenges. The paper proposes strategies to maintain data integrity, including data cleansing, validation, and explainable AI techniques. Ultimately, this research underscores the importance of robust data integrity practices to ensure the reliability and ethical use of AI/ML-powered fraud detection in the banking industry.