A Theoretical Exploration of Explainable Artificial Intelligence (XAI) in E-Commerce Fraud Detection: Paradigms, Challenges, and Future Directions
This paper embarks on a theoretical journey into the role of XAI in e-commerce fraud detection, examining its potential to transform the way fraud is detected and mitigated and explores leading-edge techniques like SHAP and LIME, unravelling how they transform opaque models into transparent decision-making allies.
Abstract
The meteoric rise of e-commerce has brought unparalleled convenience to consumers but also opened the floodgates to sophisticated fraud schemes. In this rapidly evolving digital marketplace, traditional fraud detection methods often fall short. Artificial Intelligence (AI) has stepped in as a game-changer, yet its "black-box" nature leaves critical questions unanswered: Why did the AI flag this transaction as fraudulent? Can we trust its decision? This is where Explainable Artificial Intelligence (XAI) emerges as a beacon of clarity. This paper embarks on a theoretical journey into the role of XAI in e-commerce fraud detection, examining Its potential to transform the way fraud is detected and mitigated. We explore leading-edge techniques like SHAP (Shapley Additive Explanations) and LIME (Local Interpretable Model-Agnostic Explanations), unravelling how they transform opaque models into transparent decision-making allies. Beyond just theory, we analyse the real-world integration of XAI, emphasizing its ability to enhance user trust while preserving the high accuracy demanded by e-commerce platforms. However, the road to adopting XAI is not without hurdles. From computational complexities to ethical dilemmas surrounding fairness and bias, and the challenge of making explanations accessible to non-experts, the journey is complex. Looking ahead, we chart a future where XAI becomes indispensable— powered by innovations in hybrid modelling, ethical AI frameworks, and collaborative development across industries. Through this exploration, we illuminate XAI’s profound promise: a future where fraud detection is not only intelligent but also transparent, fair, and deeply human-centric.