Sep 10, 2025
Debt recovery is becoming increasingly complex, with rising costs, low connect rates, and compliance risks straining lenders. In FY2025, India’s microfinance sector saw delinquent loans overdue by more than 31 days surge 163% to Rs 43,075 crore. Loans overdue beyond 180 days spiked from 1.6% to 5.1%, showing how deeply borrowers are struggling.
How can lenders recover faster without adding costs or risking compliance? Can AI in debt collections reduce friction while improving recovery outcomes? As the pressure mounts, AI offers lenders a smarter path forward, transforming recovery efficiency at scale.
Key Takeaways:
AI in debt collections automates repetitive recovery tasks, cutting costs and freeing human agents for complex borrower cases.
Compliance-first voice automation ensures every borrower interaction aligns with RBI standards while reducing manual audit risks.
Multilingual AI voicebots expand borrower reach across urban and rural India, strengthening trust and repayment consistency.
Retail and eCommerce benefit through AI-driven COD confirmations and return-related recovery, especially during seasonal demand spikes.
CubeRoot delivers India-focused debt collection workflows, combining automation with human-in-the-loop escalation for efficiency and empathy at scale.
What Is AI in Debt Collection?
AI in debt collection refers to the use of artificial intelligence technologies to automate borrower communication, track repayment intent, and streamline recovery workflows. Unlike traditional call centers that rely on large teams of agents and manual follow-ups, AI systems use natural language processing, voice automation, and machine learning to handle repetitive interactions efficiently and consistently.
How AI Differs From Traditional Collections
Debt recovery in India’s BFSI and NBFC sectors has often depended on field agents, manual calls, and outsourced call centers. These approaches face three critical challenges: inconsistent customer experiences, rising manpower costs, and limited scalability. AI addresses these issues by:
Automating routine interactions such as pre-due reminders, post-due follow-ups, and promise-to-pay confirmations.
Maintaining compliance with RBI and sector-specific rules through auditable scripts and secure voice logging.
Scaling instantly to handle high call volumes during month-end or seasonal repayment cycles.
Use Cases in the Indian BFSI and NBFC Context
AI is already being applied across different debt recovery needs, particularly in India, where diverse borrower segments require multilingual and scalable solutions. Examples include:
Use Case | Traditional Approach | AI-Enabled Approach |
Loan EMI reminders | Manual calls by agents | Automated voicebots sending timely reminders in regional languages |
Post-due collections | Field agent visits | AI-driven follow-ups with smart retry logic and escalation |
Compliance assurance | Manual audit checks | Built-in auditable scripts with real-time monitoring |
By applying AI in debt collections, lenders in India can lower recovery costs, ensure compliance, and reach borrowers faster, all while reducing pressure on human agents.
Why Traditional Debt Collection Struggles With Efficiency
Debt recovery in India’s BFSI and NBFC sectors is still heavily reliant on human agents, manual follow-ups, and fragmented processes. This approach creates bottlenecks that reduce recovery efficiency and increase operational costs. NBFCs may maintain profitability through loan growth and disciplined credit management in 2025, but asset quality challenges and high borrowing costs continue to strain recovery practices.
Key Inefficiencies in Traditional Debt Collection
The following pain points explain why manual methods are no longer sustainable at scale:
High manpower costs: Employing and managing large call center teams drives up operational expenses, especially for lenders handling millions of accounts.
Compliance risks: Manual audits and human-led scripts create gaps in adherence to RBI regulations, leaving organizations exposed to penalties.
Inconsistent communication: Borrowers receive varied treatment depending on the agent, reducing trust and lowering repayment rates.
Scalability limits: Recovery teams cannot easily expand during repayment peaks, leading to missed follow-ups and lost revenue opportunities.
Comparison: Manual vs. AI-Ready Approach
Factor | Traditional Collections | AI-Ready Collections |
Cost | High, driven by staff and outsourcing | Lower, automation reduces dependency on human agents |
Compliance | Manual checks, error-prone | Built-in auditable scripts and secure logs |
Reach | Limited by agent capacity | Scales instantly across languages and time zones |
Traditional debt recovery struggles not only raise costs but also limit customer experience and compliance assurance. This creates the need for AI in debt collections, which can provide lenders with scalable, compliant, and cost-effective alternatives.
How AI in Debt Collection Improves Recovery Efficiency
AI in debt collection transforms recovery operations by combining automation, personalization, and compliance-first communication. Instead of relying on manual call centers, lenders can deploy AI-driven voice agents that handle high volumes of repayment reminders, COD confirmations, and loan EMI follow-ups in multiple languages. This ensures borrowers across urban and rural India receive consistent, timely outreach, improving repayment rates while reducing operational stress.
1. Cost Reduction and Higher Connect Rates
AI reduces recovery costs and improves borrower engagement through automation and intelligent outreach. Key advantages include:
Lower manpower dependency: Automated agents handle repetitive calls, reducing the size and cost of collection teams.
24/7 outreach: Borrowers receive reminders at convenient times, boosting engagement without extra staffing.
Smarter retries: AI uses pickup patterns to schedule follow-up attempts, increasing the chance of borrower response.
High-volume handling: Seasonal repayment spikes can be managed without adding new staff or outsourcing.
2. Compliance and Consistency at Scale
Compliance remains a top concern for BFSI institutions under RBI oversight. AI ensures lenders meet strict standards by:
Enforcing script adherence: Every call follows predefined, regulation-compliant messaging, reducing the risk of deviations.
Creating secure audit logs: Conversations are stored and retrievable for regulatory audits or dispute resolution.
Reducing human error: Automated flows minimize mistakes that could expose organizations to penalties or reputational risks.
3. Customer Experience and Faster Resolutions
AI-driven debt collection is not limited to efficiency; it also improves borrower relationships. Benefits include:
Natural voice interactions: AI agents use conversational tones that reduce borrower discomfort compared to scripted human calls.
Empathy-focused design: Personalization, such as reminders in local languages, shows respect for diverse borrower segments.
Instant resolutions: Simple requests, like EMI status or COD confirmations, are resolved immediately without waiting for a human agent.
By addressing cost, compliance, and customer experience together, AI in debt collections creates measurable efficiency gains. This leads naturally to its varied applications across industries and borrower types.
Key Use Cases of AI in Debt Collection
AI in debt collections is not just about automation; it is about solving specific recovery challenges with targeted workflows. From managing delinquent accounts to supporting repayment negotiations, AI streamlines processes that traditionally required significant manpower. The following use cases highlight where lenders and businesses in India can apply AI effectively.
Pre-due reminders: Automated voicebots notify borrowers of upcoming EMIs or credit card bills, reducing the chances of missed payments.
Post-due follow-ups: AI schedules consistent outreach for overdue accounts, using retry logic to improve connect rates.
Promise-to-pay confirmations: Borrower commitments are recorded, tracked, and followed up on automatically, reducing manual errors.
Settlement offers and escalations: AI presents structured settlement options and routes unresolved cases to human agents when required.
COD collections in eCommerce: For high-volume retailers, AI confirms cash-on-delivery payments and reduces failed deliveries that add operational costs.
By covering these core scenarios, AI enables lenders, NBFCs, and eCommerce platforms to recover more efficiently while ensuring compliance and customer satisfaction.
Sector-Specific Impact of AI in Debt Collection
The effectiveness of AI in debt collections is best understood by examining its role in core Indian sectors. BFSI and NBFCs face strict compliance and recovery cycle pressures, while retail and e-commerce deal with massive transaction volumes and seasonal demand peaks. AI offers sector-tailored benefits that improve efficiency and reduce risks.
BFSI and NBFCs
Banks and NBFCs in India are under constant pressure to balance loan growth with asset quality. While India’s gross NPA ratio at scheduled commercial banks fell to a decadal low of 2.3% in March 2025, RBI projects it could rise to 2.5% under a normal scenario and even 5.6% in an adverse one. This highlights the importance of efficient and compliant debt recovery practices.
AI supports BFSI and NBFCs by:
Reducing NPA risks through automated reminders, follow-ups, and structured repayment options
Ensuring regulatory compliance with RBI-mandated standards using auditable scripts and secure call logs.
Improving recovery cycles by accelerating outreach across borrower segments, including rural areas where multilingual support is essential.
Retail and eCommerce
For high-volume eCommerce and retail platforms, failed COD collections and product returns directly impact profitability. During festive seasons, when transactions multiply, manual teams cannot scale fast enough to manage confirmations and recovery calls.
AI enables:
COD payment confirmations before delivery, reducing non-acceptance and logistics costs.
Automated return-related calls that manage customer interactions without overloading support teams.
Scalable response management during spikes, ensuring consistent communication even at peak transaction volumes.
AI in debt collections empowers both financial institutions and retailers to operate more efficiently, setting the stage for its integration into diverse use cases across industries.
Why AI in Debt Collection Needs the Human-in-the-Loop
AI in debt collections is highly effective for repetitive, rules-based interactions, but not every borrower case can be resolved by automation alone. Complex scenarios such as disputes, restructuring requests, or sensitive customer concerns require human intervention. A hybrid model where AI handles scale and humans manage complexity ensures both efficiency and empathy.
How AI and Humans Work Together
The most effective recovery strategies combine automation with selective human involvement. This approach prevents borrower frustration and builds trust in repayment processes.
AI for scale: Manages thousands of calls daily for reminders, follow-ups, and confirmations.
Humans for complexity: Step in when negotiations, dispute resolution, or financial counseling is needed.
Seamless escalation: Borrowers are transferred without repeating details, ensuring continuity in conversations.
Quick Industry Example
In the BFSI sector, an NBFC can use AI to manage EMI reminders and overdue follow-ups. When a borrower requests loan restructuring, the AI escalates the call to a human agent who can explain terms, negotiate timelines, and finalize agreements. This balance improves recovery rates without overwhelming staff.
By combining automation with human judgment, lenders achieve efficiency without sacrificing borrower trust—a balance that strengthens long-term recovery outcomes.
CubeRoot’s Advantage in AI-Powered Debt Collection
Lenders face mounting challenges with high delinquency volumes, compliance pressures, and borrowers expecting respectful yet consistent communication. CubeRoot addresses these challenges with prebuilt BFSI workflows, compliance-ready conversations, multilingual support, and human-in-the-loop escalation, enabling NBFCs and banks to scale collections without compromising trust or accuracy.
Unlike generic automation tools, CubeRoot’s domain-trained voice AI is purpose-built for Indian lenders. It delivers sector-specific outcomes that improve repayment rates, cut operational costs, and maintain regulatory discipline.
How CubeRoot Supports Lenders
Pre-due and Post-due Collections: Automates reminders, follow-ups, and promise-to-pay confirmations, significantly reducing overdue accounts.
Multilingual Voicebots: Engage borrowers in English and multiple Indian languages, bridging urban and rural repayment gaps.
Compliance-First Automation: Ensures every call follows RBI guidelines with secure audit trails, reducing regulatory risks.
Seamless Escalation: Transfers complex cases to human agents without disrupting borrower experience.
Proven Outcomes Across Functions
Debt Collection: Engage thousands of accounts daily, lower overdue portfolios by up to 35%, and reduce collection costs by 50%.
Lead Qualification: Qualify 80% of leads within minutes, cutting manual effort and boosting conversion.
Customer Support: Resolve 70% of routine queries automatically, lowering response times and support costs.
Feedback Collection: Achieve 5× higher borrower feedback rates, strengthening long-term relationships.
What if your collections could run 24/7 without expanding headcount? What recovery percentage could your NBFC achieve with AI-driven collections? CubeRoot is ready to answer that.
What could your collections team achieve if overdue accounts dropped in days instead of months? Connect with CubeRoot today and see the difference. |
Conclusion
Debt recovery is shifting from low recovery rates and high manpower costs to efficient, compliant, and scalable operations powered by AI. By automating reminders, ensuring RBI-compliant conversations, and enabling multilingual outreach, lenders can recover more while easing the burden on human teams. Borrowers receive consistent, respectful communication, and institutions gain measurable improvements in repayment cycles.
Which collections process would you automate first? How much could your team recover with AI? CubeRoot enables lenders to transform recovery efficiency while staying compliant.
Book a demo with CubeRoot today to boost recovery rates, cut collection costs by 50%, and keep compliance intact. |
FAQs
Q: How does AI improve debt recovery for lenders with large borrower bases?
A: AI automates reminders, follow-ups, and confirmations, allowing lenders to engage thousands of borrowers daily without scaling human call center teams.
Q: Can AI in debt collections reduce operational risks for NBFCs under RBI oversight?
A: Yes, AI ensures every borrower interaction follows RBI guidelines with auditable scripts, secure voice logs, and consistent regulatory compliance.
Q: How does AI handle multilingual debt collection across rural and urban India?
A: AI voicebots support multiple Indian languages, ensuring borrowers receive repayment reminders in their preferred language, improving reach and trust.
Q: What role does AI play in improving COD payment collections for e-commerce platforms?
A: AI confirms COD payments before delivery, reducing failed orders, saving logistics costs, and improving cash flow reliability for retailers.
Q: How are complex borrower cases managed when AI cannot resolve them?
A: AI escalates complex cases like disputes or restructuring requests to human agents, ensuring empathy and negotiation without breaking continuity.
Q: What measurable efficiency gains can banks achieve using AI in collections?
A: Banks can cut collection costs by up to 50%, lower overdue accounts, and improve repayment cycles through AI-driven automation.
Q: How does AI help lenders manage seasonal repayment spikes effectively?
A: AI scales instantly during peak repayment periods, sending automated reminders and follow-ups without requiring additional manpower or outsourcing.
Q: Can AI-driven debt collection improve borrower experience alongside recovery rates?
A: Yes, AI delivers consistent, empathetic conversations with faster resolutions, reducing borrower stress and building long-term trust in repayment processes.
