Plaid introduced a credit risk score based on real-time cash flow data as it dives further into credit scoring amid an industrywide push to monetize formerly unused or underused data sources. The fintech, a huge player in data aggregation, has diversified its business interests as aggregation has commoditized. Consumer-permissioned financial data shows promise as a new pipeline for consumer credit information. But the introduction of new forms of credit data doesn’t guarantee anything will change for consumers who struggle to access credit.

TransUnion has introduced new pricing for credit scoring for mortgage borrowers, undercutting the pricing of FICO’s new mortgage credit scoring model: FICO charges resellers $10 per score, while TransUnion charges $4. The market for consumer credit data and how it’s packaged is hotly contested, and the government has helped facilitate competition. In addition, the fintech Plaid, a newcomer to credit reporting, just introduced a cash flow–based scoring model. This competition is good for consumers, because it creates more ways for them to access credit. And it should also be good for data buyers, including banks, because it will mitigate prices and encourage the development of more sophisticated scoring models and data products.

The news: The average VantageScore credit score dropped one point since last month, meaning the average customer’s creditworthiness is declining. And there are other signs of credit stress that should be alarming to banks. Our take: With the average credit score dropping and delinquencies rising across all tiers—including among historically reliable superprime borrowers—financial institutions (FIs) are facing a higher-risk environment. This requires a proactive approach to risk management. FIs should tighten their underwriting standards—particularly for mortgages and auto loans, which are showing the largest increases in late payments. In addition, FIs must proactively engage with customers to help prevent delinquencies from turning into defaults. By using data to identify at-risk borrowers and reinforce customer loyalty, FIs can reach out with support and resources like loan modifications or personalized financial guidance.

Advances in AI and predictive analytics are using consumer scores to automate business decisions to predict things like risk and fraud. But concerns over fairness mean companies need to make scores transparent to consumers.