Pol, Stijn van der (2024). The Creation of an Explainable Artificial Intelligence Model to Enhance Interpretability and Transparency for ING in Their Fight Against Fraud
Abstract
This thesis presents the development of an Explainable Artificial Intelligence (XAI) model aimed at enhancing the interpretability and transparency of fraud detection systems at ING. By integrating XAI techniques, the model provides clear and actionable insights into the decision-making processes of machine learning classifiers, thereby bridging the gap between high model accuracy and the need for understandable outputs. Through extensive experimentation and validation with real-world data, this research demonstrates the practical applicability of XAI in transactional fraud detection. The insights gained from expert interviews and use case testing further reinforce the model's effectiveness in improving stakeholder communication and decision-making. This work contributes to the ongoing efforts to develop AI systems that are not only powerful but also transparent and trustworthy in combating financial fraud.