SAFE Artificial Intelligence in Finance

Abstract

Financial technologies, boosted by the availability of machine learning models, are expanding in all areas of finance: from payments (peer to peer lending) to asset management (robot advisors) to payments (blockchain coins). Machine learning models typically achieve a high accuracy at the expense of an insufficient explainability. Moreover, according to the proposed regulations, high-risk AI applications based on machine learning must be "trustworthy'', and comply with a set of mandatory requirements, such as Sustainability and Fairness. To date there are no standardised metrics that can ensure an overall assessment of the trustworthiness of AI applications in finance.

To fill the gap, we propose a set of integrated statistical methods, based on the Lorenz Zonoid tool, that can be used to assess and monitor over time whether an AI application is trustworthy. Specifically, the methods will measure Sustainability (in terms of robustness with respect to anomalous data), Accuracy (in terms of predictive accuracy), Fairness (in terms of prediction bias across different population groups) and Explainability (in terms of human understanding and oversight).

We apply our proposal to an easily downloadable dataset, that concerns financial prices, to make our proposal easily reproducible.

Keywords: Lorenz Zonoids, Accuracy, Explainability, Fairness, Sustainability, Bitcoin price prediction

JEL Classification: C01, C45