Lenders need to find the balance between improving access to finance and preventing over-indebtedness. This necessitates the use of robust predictive models, but it is often not possible to use the most predictive algorithms due to a lack of transparency.

Until recently, the adoption of advanced machine learning (ML) models in the lending industry has been hindered by an inability to explain outcomes that drive decisions. However, progress has been made in terms of improving both model predictiveness and interpretability. With the help of relevant software, it is now possible to leverage the power of ML to a greater extent in making credit decisions.

In fact, clients using such software have experienced average increases in loan acceptance rates of 20%, with limited additional risk.

In this white paper, we introduce you to the past and future of credit decision technology. By reading this, you will gain a basic understanding of the use of ML in the credit industry, including its challenges and its solutions.

We will also introduce the Evispot transparency tool, Traits.