Background

Determining a customer’s creditworthiness holds many complexities. Lenders have to decide whom to approve for credit, at what interest rate, and through which products and services. The challenge is that these critical decisions are still largely based on FICO scores, which are limited in their ability to quantify risk. This hurts lenders in multiple ways, including the risk of denying qualified customers, qualifying credit card churners, and losing business to competitors because of slow and inaccurate responses.

Opportunity

The way consumers manage their credit today has evolved from the past. Artificial intelligence (AI) and machine learning (ML) take lenders beyond FICO scores and examine alternative data sources to assess risk. For customers with limited traditional credit history, such as many millennials, lenders can evaluate credit risk based on digital footprints. An effective AI/ML strategy can help lenders draw deeper insights about customers—thereby improving credit scoring accuracy, speeding up response times, elevating customer experience, and ultimately increasing revenue.

Impact

Implementing a fast, accurate, reproducible, and cost-effective credit scoring framework is the power needed to hold a competitive edge and be a leader in the industry. See how Anaconda Enterprise can improve data science workflows in financial services by booking a demo.