Fraud Detection in the Banking Sector Using Artificial Intelligence

Abstract

The volume, frequency, and complexity of fraud in the banking industry have been escalated by the rapid growth of digital banking and online financial services. Traditional rule-based fraud detection systems which rely on fixed thresholds and rules are increasingly failing due to low levels of adaptability, high false positive rates and low capabilities to deal with new trends in fraud. Thus, artificial intelligence (AI) has become relevant as a more developed technique to improve the fraud detection process in contemporary banking institutions. It undergoes a systematic literature review of AI-based fraud detection techniques in the banking field, supplemented by an illustrative empirical case study comparing Logistic Regression, Random Forest, and LSTM models on a publicly available transaction dataset. Review papers and peer-reviewed journal articles were reviewed to study the application of machine learning, deep learning, and hybrid AI models. The effectiveness of these methods is compared regarding detection accuracy, adaptability, and real-time monitoring capability. The results indicate that AI-based fraud detection systems have better capabilities to analyze large volumes of transactional data and detect complex, non-linear fraud patterns. The empirical case study further confirms that LSTM achieves superior detection rates (96.7%) on sequential data, while Random Forest offers a practical balance of accuracy (94.2%) and interpretability. Nevertheless, critical challenges remain including class imbalance, model explainability, high computational costs, scalability limitations, and regulatory compliance. The research concludes by indicating that explainable and hybrid AI models are the way forward for sustainable and compliant fraud detection in the banking industry.