Financial institutions face a trade-off: highly accurate “black-box” AI models detect fraud well but lack transparency and pose regulatory risks, while simpler interpretable models are safer but less effective. Transformer models offer a middle ground, delivering strong fraud detection performance with improved insight into their decision-making; in this project, we further develop these models to be interpretable and compliant with regulatory requirements.
