Over the past year, artificial intelligence (AI) has dominated headlines. Tech giants have announced multi‑billion‑dollar investments in generative AI, and investors have driven the share prices of chipmakers and AI‑software companies to new heights.
That excitement has extended to the credit market, where banks and non‑banks are experimenting with AI underwriting and conversational chatbots. Yet as hype builds, signs of fatigue are emerging. According to The Financial Brand, consumers increasingly describe digital banking experiences as automated and tone‑deaf; only about 17 percent of U.S. households report being very comfortable with AI‑powered banking and nearly one quarter say they would reject these services. In a climate of economic uncertainty and strained household budgets, understanding how the AI boom really affects borrowers is essential.
Debt issuance and credit market disruption
AI’s runaway popularity is not confined to Silicon Valley. Neuberger Berman notes that AI‑related capital expenditure is driving a surge of debt issuance across public and private markets. Bloomberg estimates that U.S. technology giants could raise as much as $1.5 trillion in debt by 2028 to fund data centers and chip development. When credit markets are flooded with supply, yields can rise and spreads widen, putting upward pressure on borrowing costs for businesses and consumers. Recent reports suggest investors are becoming more cautious; the Bank of England warned that the AI infrastructure boom could heighten systemic risks. The prospect of an AI‑induced bond bubble means lenders will need to carefully manage balance sheets and stress‑test portfolios for volatility.
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AI underwriting: speed vs transparency
Within consumer lending, AI promises faster loan decisions and expanded access to credit. Modern underwriting systems parse transaction histories, employment data and even behavioural signals to estimate repayment likelihood. Borrowers who lack traditional credit histories may benefit from this holistic view. However, regulators and consumer advocates warn that algorithmic models often lack clear audit trails and may embed discrimination. LoanPro’s analysis highlights that some AI lending tools are black boxes; compliance frameworks have not kept pace and explainability is critical to meet fair‑lending requirements. Responsible lenders should pair machine learning with human oversight, ensuring that decisions are transparent and that customers can appeal or understand the reasons for denial. In the current regulatory climate, speed without transparency is a liability.
Consumer experience and AI fatigue
For borrowers, an AI‑powered future comes with mixed feelings. As digital experiences proliferate, people want efficiency without losing the human touch. The Financial Brand observed that customers prefer institutions that combine robust digital tools with authentic interpersonal service. When chatbots or underwriting algorithms fail to account for individual context, applicants feel misunderstood. At the same time, AI can help banks deliver personalised budgeting tools, real‑time fraud alerts and automated savings strategies. Striking the right balance is key: AI should augment, not replace, empathetic human advisors.
Navigating the AI credit market
So how should consumers respond to the AI boom? First, be mindful that corporate debt issuance tied to AI may push interest rates higher for everyone. When credit markets become frothy, locking in a fixed rate on a mortgage or personal loan can provide stability. Second, recognise that AI underwriting expands data sources but should not jeopardise your privacy. Before consenting to alternative data collection, read privacy notices and ensure lenders comply with the General Data Protection Regulation (GDPR) and other laws. Third, ask for explanations. If an AI model produces a decision you don’t understand, request clarity; lenders are obligated to provide adverse action notices and cannot hide behind proprietary algorithms. Finally, favour institutions that marry automation with accessibility. Community banks and credit unions that combine digital efficiency with real humans often deliver better experiences.
Responsible AI adoption could ultimately reduce uncertainty. Equifax points out that security teams are using AI to analyse large datasets, flag anomalies and reduce alert fatigue, freeing analysts to focus on genuine threats. The same principles apply in lending: algorithms can handle routine tasks while humans manage exceptions and communicate with clients. As the AI boom evolves, borrowers should remain vigilant, stay informed about market conditions and choose lenders that respect transparency and privacy.
Sources :
- The Financial Brand, “Consumers are Hitting the AI Wall: The Risk of AI Fatigue in Banking” (reporting that only 17% of households are very comfortable with AI and that customers crave human connection) – Publication date: November 2025.
- Neuberger Berman, “AI Reshaping Credit Markets” (describing how AI‑related capex is driving massive debt issuance) – Publication date: October 2025.
- Advisor Perspectives/Bloomberg, “Big Tech’s AI Debt Wave Could Overwhelm Credit Markets” (estimating up to $1.5 trillion in debt issuance and potential market impact) – Publication date: November 2025.
- Reuters, “AI‑linked Bonds Underperform Broader Market as Investors Grow Cautious” (warning of systemic risk and investor concerns) – Publication date: October 2025.
- LoanPro, “AI Trends in Lending” (highlighting lack of transparency and regulatory concerns in AI underwriting) – Publication date: August 2025.
- Equifax Insights Blog, “How AI and the Economy Are Shaping Risk, Security and Business Strategy in 2025 and Beyond” (discussing AI’s role in fraud detection and the need for governance) – Publication date: December 2025.
