AI reshapes consumer debt management, study shows

According to the study, 61% of consumers say they feel in control of their debt, while 66% are confident they understand the costs associated with their borrowing. However, only 31% know exactly how much debt they owe across all accounts. Nearly half rely on estimates, while others know balances for only some accounts or avoid calculating their total debt altogether.

The findings point to a longstanding challenge in consumer finance: fragmented financial data. Despite a growing range of digital banking and personal finance tools, many consumers still struggle to maintain a consolidated view of their liabilities across multiple lenders and accounts. For financial institutions and FinTech providers, this creates an opportunity to deliver solutions that bring together account information, automate debt tracking, and provide actionable repayment insights.

The research also highlights a disconnect between consumers’ intentions and their financial behaviours. While 77% say they are willing to make sacrifices to become debt-free sooner, only 29% regularly pay more than the minimum amount due on their debts. When asked how they would use an unexpected $1,000, fewer than four in 10 respondents said they would use the entire amount to repay debt, with many preferring to divide the funds between debt repayment and savings.

For FinTech providers focused on debt optimisation and financial wellness, these findings suggest growing demand for tools that help consumers prioritise repayments, visualise the cost of debt, and evaluate trade-offs between saving, spending, and borrowing. The challenge is not simply helping consumers understand what they owe, but helping them determine the most effective use of available funds.

Debt visibility remains a significant issue. More than six in 10 consumers reported discovering at some point that they owed substantially more than they had initially believed. The problem is particularly pronounced among younger consumers, with 80% of Gen Z respondents reporting that they had underestimated their debt levels. These findings reinforce the need for real-time financial data connectivity and automated debt monitoring capabilities.

Beyond debt management, the study found evidence of increasingly complex financial behaviours. Some consumers reported borrowing to invest, participating in sports betting and prediction markets, or delaying debt repayments to pursue higher-risk financial activities. Such behaviours blur the lines between spending, investing, and borrowing, making personalised financial guidance more important for both traditional financial institutions and FinTech platforms.

Against this backdrop, AI is emerging as a key enabler of next-generation financial services. Nearly half of respondents (46%) said they have used AI tools such as ChatGPT, Gemini, or Claude for personal finance-related purposes. Current usage is largely focused on financial education, budgeting assistance, cost-saving recommendations, and general financial guidance.

However, the research indicates that AI adoption is beginning to move beyond advice and into execution. Among consumers who use AI for personal finance, 30% reported using it to help pay bills, signalling a shift towards more action-oriented financial experiences. This trend aligns with the broader concept of agentic finance, where AI helps consumers progress from decision-making to transaction completion within a single workflow.

Consumer trust in AI is also evolving. The survey found that 64% of respondents trust AI at least somewhat to provide accurate financial information and recommendations. Trust levels are notably higher among Gen Z and Millennials. Yet confidence drops when AI is asked to perform financial actions autonomously, such as moving money, making payments, or applying for financial products on a consumer’s behalf.

According to Spinwheel, the next phase of AI-driven financial services will depend on closing three key gaps. First, AI systems need access to a complete and accurate picture of consumers’ financial lives. Second, financial institutions must bridge the divide between AI-generated recommendations and the actions required to execute them. Third, solutions must be designed to keep consumers in control of critical financial decisions while allowing AI to automate routine tasks.

For banks, lenders, and FinTech, the findings suggest that the future of debt management lies at the intersection of data aggregation, AI-powered guidance, and embedded financial actions. As consumers increasingly seek personalised support for managing debt and achieving financial goals, platforms capable of connecting fragmented financial data with intelligent, actionable experiences may gain a competitive advantage in the evolving digital finance landscape.