Fraud detection in financial transactions using LSTM and XAI with ontological validation

Abstract

The escalation of digital financial services has increased the occurrence of credit card fraud, demanding the development of advanced, trustworthy fraud detection systems. Traditional and simple rule-based and classical machine learning techniques, while useful, often fail to detect complex, evolving fraud patterns and lack sufficient interpretability for high-stakes financial decision-making. This research proposes a novel, integrated approach combining Long Short- Term Memory networks, Explainable AI techniques, and ontology-based semantic validation to address these challenges. The Long Short-Term Memory model captures sequential transaction behaviors effectively, identifying anomalies indicative of fraud. To overcome the inherent black- box nature of deep learning models, Local Interpretable Model-agnostic Explanations is employed, providing transaction-level interpretability and enabling stakeholders to understand the factors behind each prediction. Further, an ontology is developed to embed domain-specific knowledge, offering semantic validation of the model outputs. This ensures that predictions not only rely on learned patterns but also align with predefined risk rules and expert knowledge. Experimental results demonstrate high accuracy and recall rates, confirming the model’s effectiveness in detecting fraudulent activities, while the ontology layer enhances trust and reliability. This hybrid framework thus advances fraud detection by combining predictive power, explainability, and semantic validation, contributing to the development of more secure and transparent financial systems.