FICO

Consumer advocates and fintechs say the traditional credit score and lending systems that use it are murky and perpetuate bias. FICO’s leader points out that new FICO scores use expanded data sets, just as fintechs do.

Variables unique to the past year, such as the effect of government stimulus, are not fully considered in typical credit scores. For a more complete picture, the industry should accelerate a digital transformation in scoring models.

Without the application of tested safeguards, using such data can run the risk of worsening the very problems of credit access that we seek to solve.

TomoCredit relies strictly on cash-flow data to gauge the creditworthiness of immigrants and young consumers, who often have too little financial history for traditional scoring models.

Harvest, a fintech founded by Nami Baral, has developed an alternative scoring method that amasses data on spending patterns, debt payments and even earnings potential to get a better sense of consumers' creditworthiness.

Lenders are struggling to deploy deposits into interest-earning assets, and changes to post-secondary education may further limit these options.

The German bank agreed to pay $150 million to New York State for its dealings with Jeffrey Epstein; the new tool will help lenders determine which borrowers are in the best shape to weather a crisis.

Leveraging AI and machine learning can help credit unions focus on doing what they do best: forging connections with members.

Attempts to rewrite the method for determining unintended discrimination in housing should not bleed into fair-lending laws.

Testing shows an overreliance on machine learning data can unfairly harm credit scores.