India's digital lending ecosystem has grown at an unprecedented pace over the last few years; 44 mn new to credit borrowers have entered the formal credit system as of February 2026 with access to loans through digital channels. This expansion has been a significant step toward financial inclusion, but it has also brought a new challenge into focus: how do lenders scale recovery operations responsibly as credit volumes increase?AI (Photo credit: Unsplash)For a long time, collections were viewed primarily as a backend operational function. The objective was simple: recover dues as quickly as possible. But the lending industry has evolved. Regulators, consumers and even lenders today expect recovery practices to be transparent, respectful, and compliant. We have seen that happening first hand with lenders coming to us asking for a compliant-first recovery process that is baked with advanced technology and infrastructure. This has never happened before. Recovery can no longer be approached as a volume game. It is increasingly becoming a tripartite trust exercise between lenders, borrowers and collection agencies.This is where Artificial Intelligence (AI) is beginning to play a critical role.When people think about AI in lending, they often think about underwriting or fraud detection. Recovery is rarely the first area that comes to mind. Yet, some of the most impactful applications of AI are emerging in the collections ecosystem because recovery is fundamentally a decision-making and communications problem. It requires understanding borrower behaviour, identifying risk signals, determining the right engagement strategy, and ensuring that every interaction stays within policy and regulatory boundaries.One of the biggest misconceptions in debt recovery is that all overdue borrowers are the same. In reality, they are not. Each borrower is unique and so are their reasons to not pay on time. Borrowers fall into very different categories, from those facing temporary financial setbacks to those intentionally avoiding repayment. Yet, traditional recovery processes have often treated them similarly, leading to poor borrower experiences and sub-optimal recovery outcomes.We see AI fundamentally helping change that.By analysing past credit history, repayment patterns, communication behaviour, account activity, and historical interactions, AI models help lenders understand borrower context better. Instead of relying on a one-size-fits-all collections process, lenders can now prioritise accounts differently and engage borrowers in a more contextual and personalised manner. Different delinquent pools, different engagement strategies. AI helps identify the borrower cohorts with higher likelihood to pay, and reach a wider audience at once. Today, we have the ability to reach 1Mn accounts on a daily basis with the help of AI in a more nuanced manner. Hence with AI, we are not only able to scale but also able to personalise with context thereby recovering more and faster. The difference between successful recovery and prolonged delinquency is not persistence. It is understanding the borrower well and maintaining that unified borrower context across channels all the time. This was absolutely missing a few years ago. But today, we are able to achieve that.However, efficiency alone is not enough.One of the most significant shifts we are seeing is AI's role in strengthening compliance, not just improving recovery outcomes. Traditionally, compliance checks happened as an afterthought tied to the end of the process. Today, AI enables compliance to be embedded directly into recovery workflows. Recovery platforms can now enforce communication policies, maintain audit trails, and identify deviations automatically, reducing reliance on manual oversight. As regulatory expectations around customer treatment and accountability continue to rise, the most effective AI systems are not those making independent decisions, but those operating within clearly defined boundaries. We call this "intelligence with guardrails" where AI provides scale and consistency while human judgment remains firmly in control. While experienced recovery agents bring valuable judgment, they cannot review thousands of accounts or monitor compliance requirements in real time. AI fills that gap, enabling a more scalable, accountable, and empathy-first approach to debt recovery. Perhaps the most overlooked benefit of AI in recovery is its potential to improve borrower experience.Collections have historically been one of the least customer-friendly aspects of lending. Borrowers often receive generic reminders - harsh ones, repeated follow-ups, or communications that do not reflect their individual circumstances while putting borrower dignity at risk. AI makes it possible to move away from that traditional approach. AI gets you the context. AI enables personalisation at scale. AI self-learns as more insights are fed back into the platform in real-time.When lenders better understand borrower intent and financial behaviour, conversations become more relevant and constructive. In our experience, “right channel, right message, and right time” is the key which is now absolutely possible with AI. Recovery then becomes less about pressure and more about resolution.Of course, none of this suggests that AI is a silver bullet. Poorly designed models can create new risks, particularly if they operate without transparency or adequate oversight. The focus therefore should not be on deploying more AI, but on deploying responsible AI. Governance frameworks, auditability, model monitoring, and human review mechanisms are becoming just as important as the algorithms themselves.The future of debt recovery will not be defined by who automates the fastest. It will be defined by who builds the most trusted systems.As lending continues to expand across India, fintechs have an opportunity to rethink recovery from the ground up with AI at its core. AI gives the industry the tools to become more ethical, and efficient at scale, but its real value lies in helping lenders become more fair, more compliant, and ultimately more trustworthy thereby bridging the trust deficit between lenders and borrowers.(The views expressed are personal)This article is authored by Ananth Shroff, founder & CEO, DPDzero.