Moving Towards Reliable Artificial Intelligence in Finance

Thanks to the accessibility of machine learning (ML) techniques, financial technologies are growing in almost every facet of finance, including asset management (robot advisors), payments (blockchain currencies), and peer-to-peer lending. High accuracy is sometimes attained by machine learning models at the price of inadequate explainability. Furthermore, high-risk AI systems dependent on ML should be “trustworthy” and adhere to a collection of obligatory standards, including sustainability and fairness, as per the proposed legislation. As of right now, no standardized measures exist that can facilitate a thorough monitoring of the reliability of AI implementations in the financial industry. To close the lag, we provide a collection of collected statistical techniques depending on the Lorenz Zonoid approach which may be utilized for evaluating and tracking the reliability of an AI application over time. In particular, the techniques will assess Explainability, Accuracy, Fairness and Sustainability.