A new study from King’s Business School at King’s College London and the Federal Reserve Board reveals that automated credit-limit increases by banks are significantly contributing to rising household debt in the United States. The research, titled "Automated Credit Limit Increases and Consumer Welfare," shows that about 80% of credit limit increases are initiated by banks through algorithms rather than requested by consumers. These automatic adjustments add more than $40 billion in new available credit each quarter, with the majority going to borrowers who already carry outstanding balances. The study finds that when credit limits are raised without a request, borrowers tend to increase their revolving debt by approximately 30%, indicating that algorithm-driven decisions are a key, often invisible, force behind growing personal debt. The research, published in the Finance and Economics Discussion Series, is based on detailed regulatory microdata covering over 70% of the U.S. credit-card market, collected through the Federal Reserve’s Capital Assessments and Stress Testing framework. The findings show a clear pattern: banks are more likely to raise credit limits for customers who are already in debt. In fact, one-third of all unpaid credit-card balances in the U.S. can be traced to limit increases made after the account was opened, and this figure rises to 60% among borrowers with lower credit scores. Banks that frequently highlight their use of AI and machine learning in financial disclosures are also the most active in deploying automated limit increases. The researchers tested policy models from the United Kingdom and Canada, where banks must obtain customer consent before increasing credit limits, especially for those already in debt. Their analysis suggests that adopting similar rules in the U.S. could improve consumer welfare by about 1%, reduce revolving debt, and lower the share of income spent on interest payments—while having only a minimal impact on overall credit availability. Dr. Agnes Kovacs from King’s Business School emphasized the hidden influence of these automated systems: “Banks are using increasingly sophisticated models to predict who will borrow more if their limit is raised. For many consumers, this means an automatic increase they never asked for and may not fully understand. These decisions are shaping household debt across the country in ways most people don’t see.” While automated credit increases can improve access to credit and help households manage spending, the study warns that targeting those already in debt leads to higher borrowing and greater financial risk. The findings highlight the need for stronger consumer protections in the era of data-driven finance. The European Union is set to implement similar regulations next year, underscoring a growing global recognition of the need to balance innovation with consumer safety.