Businesses turning to AI debt collection may be sacrificing significant results and customer trust, according to new research from one of the Worlds top Universities.
As artificial intelligence reshapes industries from finance to customer service, many companies have eagerly adopted AI debt collection tools to automate recovery processes.
But a ground breaking 2025 study led by Yale School of Management professor James Choi reveals a sobering truth: AI debt collection is significantly less effective than traditional human-managed methods.
This comprehensive research study—spanning two years and analysing data from over 300 debt collection firms—found that human agents outperformed AI tools in debt recovery rates, customer responsiveness, and long-term satisfaction.
Key Findings: Why AI Debt Collection Isn’t Delivering
In Choi’s analysis, human collectors secured 23% more debt repayments compared to AI-based systems. While AI debt collection tools promised lower operational costs and automated scalability, they failed to match the interpersonal skills and adaptability of trained human agents.
“AI can optimize timing and language, but it can’t replicate empathy,” said Choi. “Debt collection isn’t just about numbers—it’s about communication, trust, and understanding the person behind the debt.”
Additional insights from the AI Debt Collection study include:
Debtors were 34% more likely to respond to human outreach than AI-generated messages.
AI systems had higher complaint rates, often due to poorly timed messages or repetitive, impersonal interactions.
Customer satisfaction and retention were significantly lower for accounts managed through AI tools.
Why Human Debt Collection Still Works Better in 2025
While AI debt collection platforms use machine learning to predict debtor behaviour and customize outreach, they still operate within fixed parameters. Human collectors, by contrast, can assess context in real-time—adjusting tone, proposing flexible payment plans, or offering emotional support when necessary.
“An AI may send a reminder,” said Carla Mendoza, Director of Operations at Horizon Collections. “But only a person can listen to a single mom explain why she missed a payment—and come up with a workable solution.”
This human flexibility not only increases collection rates but also preserves relationships with customers—particularly important for Small Businesses, banks, healthcare providers, and subscription-based services.
The Hidden Costs of AI in Debt Collection
One of the most compelling arguments for using AI in collections is cost. Automated systems can operate 24/7 with no salaries or benefits. However, Choi’s report highlights that these savings are often offset by lower recovery performance and reputational damage.
In one notable example, a European fintech firm was fined €2.5 million in 2024 after its AI debt collector sent manipulative messages in violation of EU consumer laws. Others have reported AI systems continuing to send payment requests even after debts were cleared, resulting in negative reviews and customer churn.
“When an AI system goes wrong, it can be hard to diagnose and fix—especially if the algorithm was trained on incomplete or biased data,” Choi explained.
Compliance and Regulatory Risks
Debt collection is subject to strict legal regulations. In the U.S., the Fair Debt Collection Practices Act (FDCPA) governs how and when collectors can contact debtors. In the EU and UK, GDPR, FCA and consumer protection laws for recovering unpaid debts also apply.
AI tools must be meticulously programmed to comply—but when violations occur, the consequences can be serious.
“Human collectors can be trained, monitored, and held accountable,” said consumer law attorney Angela Kim. “But when an AI misbehaves, liability is harder to assign, and the reputational risk is massive.”
Is a Hybrid Approach the Solution?
Some firms have adopted hybrid AI-human models, allowing AI to manage initial contact while human agents handle complex cases. However, Choi’s study raises concerns even about this compromise.
“If the first interaction is negative—like a cold or aggressive AI message—it can sour the entire customer experience, making human recovery efforts harder,” said Choi.
Hybrid systems can also confuse debtors, who may not know whether they’re speaking to a person or a bot, leading to frustration and mistrust.
What Businesses Should Do Instead
Rather than replacing human agents with AI, the study recommends a tech-enabled but human-led approach. Key recommendations for 2025 include:
Invest in training human agents to better understand debtor behaviour.
Use AI tools to support—not replace—agents, such as CRM enhancements or data analytics for prioritization.
Focus on ethical, empathetic collection practices that align with brand values and consumer expectations.
Monitor and audit all AI communications to ensure compliance and avoid reputational harm.
Final Verdict: AI Debt Collection Is Not Ready to Replace Humans
The rise of AI debt collection promised faster, cheaper, and more scalable recovery efforts.
But Yale’s study reveals that, at least in 2025, those promises remain largely unfulfilled. From lower recovery rates to increased legal and reputational risks, businesses may find more value in refining and empowering their human debt collection teams.
“Technology should amplify human intelligence, not replace it,” Choi concludes. “When it comes to collecting debts, there’s no substitute for the human touch.”
About the Study
Professor James Choi’s full report, “AI vs. Humans in Debt Recovery: A Behavioral and Performance Analysis“, will be published in the July 2025 issue of the Journal of Behavioral Economics and Organizational Studies.
