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Santander Enhances Credit Risk Evaluation with FICO Using AI

Risk Analytics

Business Honor
09 May, 2025

Santander applies machine learning to improve credit decisions, risk evaluation, and loan pricing efficiency.

The 2025 FICO Decisions was awarded to Santander Consumer USA for changing its approach to credit risk management. By using advanced data analytics and machine learning from the FICO Platform, Santander has significantly improved how it evaluates credit applicants, making its risk assessment faster, more accurate, and more open to market changes.

Older risk tools presented problems for Santander which oversees a $60 billion loan portfolio and provides services to more than 3 million consumers. These legacy systems required time-consuming manual updates and struggled to keep pace with rising interest rates and higher vehicle prices.

Santander now uses machine learning in three key areas of credit risk: determining whether to approve or decline credit applications, assigning specific reasons when applications are rejected (known as adverse action), and calculating the likelihood that a borrower might default on a loan an important factor in setting appropriate pricing.

Santander has decreased the workload for its data teams and expedited decision making by programing and reorganizing these procedures. Customers will receive speedier responses as a result and the business will be able to respond to changes in the economy more speedily. Santander's creativity was commended by FICO, which claimed that it changed a laborious, manual procedure into a seamless, actual system.

In addition to increasing productivity, this modification has increased the accuracy and updateability of the company's risk models. According to Santander Consumer USA CEO Bruce Jackson, FICO's assistance freed up their teams to focus more on data analysis rather than creating models from the ground up. This award proves how, in the ever-evolving financial world of today, lenders like Santander can stay active, lower losses, and make better lending decisions with the aid of modern risk analytics.