Lending is a business of collecting.  

The real work starts once a loan is disbursed, and monthly auto-debit requests start hitting your borrowers’ bank accounts. It is then that the grunt work of actually separating wheat from the chaff begins. A timely paying borrower is a blessing to the lender, but the problem of delinquencies is increasingly getting worse – especially in certain buckets and portfolio segments.  

The traditional view of collections being a business-adjacent function needs to be discarded, and it’s time that it gets the priority it deserves.  

Traditional collections operate in a world of lagging indicators. By the time a customer misses their third EMI, the damage to both the recovery prospects as well as the relationship is often irreversible. What our collection models seem to be operating on is, first, wait for delinquency, then scramble to recover what is already at risk. 

This reactive approach is fundamentally at odds with today’s digitally native borrowers. They are overwhelmed by choice, empowered by information, and incredibly resistant to one-size-fits-all all recovery attempts. Aggressive phone calls and intimidating field visits simply aren’t effective enough – there are regulatory frowns to deal with as well as the potential of a customer relationship going for a toss.  

 And this is why most progressive lenders should rethink collections and pivot towards an AI-driven approach.  

 The fundamental insight that is driving this AI-first collection approach is deceptively simple: the best collection strategy may not be collection at all!  

Intelligent collections will be built upon the 3Ps framework: prediction, prevention, and partnership.  

AI-Powered early warning system:  

An AI-powered EWS (Early Warning System) can detect distress signals months before the first EMI payment is missed. Account Aggregator integration for its account monitoring use case is another novel way of leveraging tech for real-time cash flow updates. When a customer transaction velocity changes or spending patterns shift dramatically , AI can flag potential distress, not as compliance alerts, but as relationship opportunities.  

This is a borrowed approach from renowned American banks like JP Morgan, where the collection process is driven with empathy rather than intimidation. A customer whose salary deposit becomes irregular or whose expense pattern changes suddenly gets flagged for intervention, not punishment. 

Behaviour of customers with alternate data:  

Digital behaviours are another strong indicator of a customer’s financial pulse. A customer who stops using banking apps, avoids payment reminders, or is in search of debt consolidation options is crying for help. The problem is that you, as a lender, are unable to hear this.  AI can help interpret these signals and act upon them proactively. Combining digital behaviour with an external correlation engine can add another layer. Job market volatility in specific sectors, regional economic stress, and seasonal income variation all predict portfolio-level stress before individual default manifests. 

Instead of waiting for the customer to miss payments, lenders can proactively intervene and offer restructuring options before stress turns into default. 

Evolving methods, evolving platform feat. WhatsApp:  

Speaking of evolving preemptive measures, the channels of collections ought to evolve, too. The battleground for recovery has already shifted to platforms where customers actually engage. In India, this means WhatsApp.  

AI-powered WhatsApp collections can create conversational experiences that feel more supportive and less adversarial. A conversation flow that can adapt in real time based on a customer’s response will be of paramount importance.  

A customer who immediately acknowledges a payment reminder gets a different path than ones who seem confused or frustrated. Sentiment analysis understands not just what customers say, but how they feel.  

Rich media capabilities can let customers update payment status, request extensions, or access self-service options directly within the chat. This can potentially transform negative interactions into positive service experiences.  

Collection conversations can become relationship-building opportunities rather than confrontational encounters. 

The AI voice for collection:  

Voice AI has the potential to take this a step further. AI calling bots are promising in their abilities to be able to conduct genuinely empathetic conversations, understand context, emotions, and intent. These are not the robotic, frustrating experiences of early automation. NLP enables these bots to understand meaning, not just words. And when customers express temporary financial difficulty, AI can recognise income disruption and adjust the conversation flow accordingly. Or redirect to a human to help build a tailored resolution. 

Emotion detection through voice patterns can potentially identify stress, anger, or confusion in real-time, triggering appropriate responses or human handoffs. Dynamic negotiation capabilities can work within pre-defined parameters to find mutually acceptable solutions, payment plans, adjusted due dates, and specialist escalations. 

AI x Human Judgement:  

Traditional collection strategies of waterfalls are giving way to dynamic, AI-driven prioritisation.  

Instead of the old 30- 60- 90-day buckets, smart allocation can consider recovery probability based on customer profiles and behavioural indicators. Relationship value metrics factor in lifetime value and cross-selling potential. Capacity-to-pay assessments use real-time financial health indicators. Channel effectiveness prediction determines the most successful outreach method for each individual customer. 

AI insights, but guided by human judgment, receive rich context about each customer interaction: emotional state, communication preferences, financial situation, and optimal next steps. This creates a powerful symbiosis where technology amplifies human capability rather than replacing it. 

The empathy layer beneath the AI-first collection model:  

Progressive lenders see AI-powered collections as more than cost centre optimisation. It's becoming a strategic differentiator and customer intelligence engine. Understanding why customers struggle with payments informs better product design and pricing strategies. Deep customer conversations reveal cross-selling opportunities. Temporary cash flow issues might indicate the need for overdraft facilities; consistent partial payments might suggest better-sized loan amounts. 

Surprisingly, customer loyalty often increases after positive collection experiences. Customers who receive empathetic, solution-focused interactions become more engaged than those who never face payment issues. 

As these capabilities expand, regulatory considerations become crucial. RBI's evolving guidelines on digital lending, data protection requirements, and fair practice codes all impact AI deployment in collections. The key is building systems that are transparent in decision-making, auditable for compliance, respectful of customer privacy, and fair across different customer segments. 

The technology is largely here. The question isn't whether AI will transform collections; it's how quickly organisations can adapt to their processes, people, and perspectives. 

I envision a future where collections become predictive customer care. Recovery conversations happen through preferred channels with respect and understanding. Data insights drive not just collection strategies, but even product design and customer experience. 

As technology partners with some of the savviest lenders, our focus while building remains to keep this in focus - we are not just collecting what's owed; we're preserving relationships while protecting business interests. AI gives us the tools to do both, but only if we're bold enough to reimagine what collections can become.