Traditional credit scores don’t always tell the full story of a borrower’s financial health. Consumers who manage their money responsibly may still fall short of conventional scoring thresholds, while newer financial services generate data that traditional models may not capture. As a result, lenders are increasingly looking beyond credit scores to assess borrowers.
Indonesia’s digital-first Superbank, for example, is leveraging artificial intelligence and behavioral analytics to assess borrowers, with the aim of reaching consumers who may have been overlooked by traditional credit scoring models.
The approach is intended to help Superbank, Indonesia’s third-largest digital bank by assets, make more informed lending decisions, tailor products to underserved segments, and manage risk.
Benefiting “Near-Miss” Borrowers
Superbank’s approach reflects a broader global trend toward alternative credit scoring.
Payments giant Block recently made its proprietary Cash App Score available to other lenders. The score draws on real-time data from the Cash App ecosystem, including spending, saving, repayment, and peer-to-peer activity.
Mortgage lenders are also looking beyond traditional scoring models to expand access to credit. In the U.S., Fannie Mae and Freddie Mac have embraced alternative credit scoring and lowered minimum credit score requirements.
These efforts aim to expand homeownership for “near-miss” borrowers—people who may have documented income or cash reserves but whose credit scores fall below conventional thresholds.
Building a More Complete Financial Picture
The common thread is an effort to give lenders a more comprehensive view of borrowers’ financial health, helping them make better-informed underwriting decisions.
Several factors are driving this shift. Inflation and soaring credit card debt have put pressure on household finances, while the growing range of financial services—from fintech accounts to buy now, pay later loans—has created additional sources of financial data.
Although these services can offer welcome alternatives for consumers, their activity may not be fully reflected in traditional credit scores. And because of that, lenders may lack a complete picture of how consumers manage their money and meet their financial obligations.
Alternative scoring methods, however, bring their own challenges. Conventional credit scoring models are well established and have proved effective over decades. Introducing new data sources and technologies like AI also raises questions about accuracy, data privacy, and regulatory compliance.
Superbank has emphasized that its use of AI will remain subject to data protection requirements and regulatory oversight, with human judgement continuing to play a role in lending decisions.