We propose a general methodology framework for eXplainable credit scoring to provide interpretability of each individual variable and measure fairness. Specifically, it is able to detect important variables and quantifies their individual impact on a firm’s credit classification via the...

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...

Explanations are necessary objects of a decision-making problem. However, complex Machine learning (ML) models that are usually used to provide decisions lack explanations. Model-agnostic explanation methods are the solutions for this problem and can find the contribution of each variable to the...

Financial institutions are increasingly leveraging on advanced technologies, facilitated by the availability of Machine Learning methods that are being integrated into several applications, such as credit scoring, anomaly detection, internal controls and regulatory compliance. Despite their high...

Though gender equality has been at the centre of debate over the last decades, a number of benefits concerning the impact of female directors on corporate performance are still overlooked. Particularly, the link that seems to exist between female directors and sustainable finance has received...

Considering the attention placed on SMEs in the new Basel Capital Accord, we propose a set of Bayesian and classical longitudinal models to predict SME default probability, taking unobservable firm and business sector heterogeneities as well as analysts’ recommendations into account. We...