BETTER RISK ASSESSMENT WITH BEHAVIORAL ANALYSIS

Our client, an international bank, sought to reduce the default rate for small loans in emerging markets. Modeling risk in this environment presented a significant challenge for the bank, as most customers had a limited digital footprint.  As a result, less reliable demographic information was used to assess creditworthiness.

INTELLIGENT APPLICATION OF ALTERNATIVE DATA

We developed a risk prediction engine that more appropriately determined credit risk by basing it on customer behavior. Our models used a variety of data sources to better identify and understand the key behavioral indicators that drive loan repayment.

Integrating our model into the underwriting process cut the bank’s default rate in half, and is projected to create savings of tens of millions of dollars annually.