By Shantanu Gangal, Co-Founder and CEO of Prodigal
Loan servicing has historically favored scale. Large loans like mortgages, auto loans, and big personal credit lines justify the cost of human agents and compliance overhead. Small-balance accounts often cannot absorb these costs, especially given the unpredictability and time-intensive nature of borrower conversations.
For decades, this stubborn economic equation made every small-balance debt simply not worth pursuing. The cost of collection often exceeded the value of the account, and lenders wrote off millions in receivables while borrowers were left carrying long-term credit consequences. Agentic AI breaks that equation entirely.
The tiered reality of financial services
Traditional collections models were built around human efficiency and have always operated in tiers. A collections agent has a limited amount of time and attention to allocate across accounts. Naturally, lenders prioritized accounts where recovery value justifies the operational cost. This means that small-ticket balances like missed BNPL payments or delinquent utility bills are too minor to justify individual attention and rarely receive meaningful engagement. Human agents simply cannot afford the time required to handle these interactions.
Borrowers often pause, repeat themselves, switch languages mid-call, or require reassurance before committing to a repayment plan. These are normal behaviors in financial conversations, but they are costly when every minute of an agent’s time matters. Human agents in collections operate under average handle time pressure, which creates an incentive to cut conversations short. That structural constraint is what made small-balance servicing economically indefensible.
If you are a high-value credit card customer spending heavily every month, you receive a concierge-level experience. When you call your lender, you speak to a highly trained agent who understands your history, communicates clearly, and resolves issues quickly. That level of service has always been reserved for premium relationships. Agentic AI makes it possible to deliver the same quality of service across all segments.
Unlocking the viability of micro-credit
AI systems bring something uniquely valuable to these interactions: patience, consistency, and scalability. An AI agent operates under no handle-time pressure. It can sit with a hesitant borrower for several minutes without that being a cost. It can repeat an explanation, absorb a pause, switch languages, and return to the resolution objective without losing its place. In effect, AI makes it possible to deliver a consistent, high-quality servicing experience regardless of how large or small the outstanding balance is.
BNPL companies that once pushed borrowers into clunky self-service portals and hoped for the best are now seeing even their smallest balances handled personally and proactively. Resolutions that previously required three or four outreach attempts are increasingly happening in a single interaction, and recovery rates are climbing as a direct result. This is the predictable outcome when you remove the cost structure that bucketed service quality by account value. The concierge experience requires concierge economics. For most of the consumer credit market, those economics have never existed, until now
A shift in consumer expectations
The broader implication is that servicing quality in lending may no longer be tied to consumer tiers. Historically, premium service experiences were limited to the highest-value relationships. As AI becomes more embedded in the servicing process, consumers are beginning to expect faster, more responsive interactions across all lending relationships. For lenders, this represents both an operational change and a strategic opportunity. By engaging borrowers earlier and more consistently, they improve recovery outcomes while reducing the number of accounts that escalate into long-term credit issues.
Where Agentic AI makes the difference
The category of AI now emerging in collections is not the rule-based chatbot of five years ago, nor the simple speech-analytics layer that flags compliance keywords after the fact. Agentic AI systems are capable of conducting full, adaptive conversations by listening, interpreting intent, adjusting tone, navigating objections, offering payment options, confirming arrangements, and documenting outcomes.
What separates these systems from earlier automation is the ability to maintain goal-directed behavior across a full conversation. Rather than following a fixed script, an agentic system holds the collection objective while dynamically adjusting its path by offering a hardship deferral when the borrower signals financial stress, switching languages mid-call, or stepping down to a payment plan when a lump-sum offer is declined. The system tracks conversation state, interprets intent signals, and selects the next action from a policy rather than a flowchart. That is a structural difference from scripted IVR or keyword-matching automation.
One of the most immediate and measurable impacts is wait time reduction. A borrower calling to resolve a balance connects within seconds no queue, no hold music. For a borrower with a small BNPL delinquency calling on a Sunday evening, or a gig worker trying to resolve an auto loan arrangement at 11pm, availability is the difference between a resolved account and a continued delinquency. Premium lenders are now beginning to associate their brand with that kind of responsiveness, and the only way to deliver it at scale is through AI.
Where innovation meets regulation
Agentic AI systems in collections are not plug-and-play. They require deep integration with loan management systems, rigorous compliance validation across applicable regulatory frameworks, and extensive testing across the full range of borrower conversation types. The lenders who will scale without regulatory exposure are the ones who instrument compliance into the system design rather than treating it as a post-hoc layer. Borrower experience and regulatory integrity are not constraints on performance. They are conditions for sustainable performance.
For the first time, the economics of collections align with the borrower experience. Not because lenders became more generous, but because the cost structure that forced the tradeoff no longer exists.