Delete search term

Header

Quick navigation

Main navigation

Towards Explainable Artificial Intelligence and Machine Learning in Credit Risk Management

At a glance

Description

For Switzerland, the reputation of stable long-standing financial institutions has always represented a crucial advantage for the acquisition and retention of customers. However, as the financial service industry becomes more global, clients expect personalized offerings and inclusiveness of service which in turn requires lenders to rely extensively on alternative data and advanced modelling techniques.

Swiss financial intermediaries are falling behind in using alternative data and novel Artificial Intelligence (AI)-driven credit scoring methodologies. Partially, this is due to the large legacy IT landscape which might not be accommodating for advanced analytics. An even more relevant barrier for wider adoption of AI in the context of the Swiss finance sector is related to the concept of explainability. Namely, AI solutions are often referred to as “black boxes” because typically it is difficult to trace the steps the algorithms took to arrive at its solution and understand how they have made their decisions. This challenge is particularly relevant for Swiss financial intermediaries: both traditional and FinTech companies face similar challenges in adhering to data-related regulations such as the General Data Protection Regulation (GDPR) which provides a “right to explanation”, enabling users to ask for an explanation as to the decision-making processes affecting them. This impacts their ability to develop AI-based solutions.

We propose building a visual analytics (VA) tool, specifically tailored to the context of credit risk evaluation, useable for both model developers (i.e. Swiss financial intermediaries operating in the commercial or consumer credit space) as well as model evaluators (Swiss regulatory bodies that have to validate the models). More importantly, such a visual tool will enable model evaluators, a non-technical audience, to gain some insight into how AI models applied to credit scoring work and identify the reasons behind the decisions taken.