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Bharat Fintech Summit 2026

Redefining Collections: The Al Revolution Every Lender Needs to Know

The lending industry has undergone a remarkable transformation over the past decade, driven by advancements in technology and the increasing integration of data. Today, 50–70% of loans are sourced through digital channels, while automated underwriting has reduced approval timelines from a few days to just a few hours. Simplified onboarding processes, the use of alternate data-based lending, and the adoption of data-driven decisioning models have expanded credit access to a broader range of customer segments, including those with limited or no credit history. However, despite these advancements, one critical area remains underserved: collections—a function that continues to depend on outdated, resource-heavy processes, limiting its ability to meet the demands of modern lending portfolios.

While these advancements bring immense opportunities, they also expose lenders to heightened risks. The very technologies that enable broader credit access also increase the complexity of managing portfolios, particularly for borrowers with limited financial resilience. Evolving credit models and alternate data sources, while promising, introduce the risk of inaccuracies or unforeseen borrower behaviors. Furthermore, with delinquencies rising in certain segments, the inability to modernize collections poses a significant threat to profitability, as lenders face challenges in identifying defaulters early and recovering dues efficiently. In lending, every reward comes with its share of risks—and for lenders, collections remain the Achilles’ heel of credit enablement.

Collections, the backbone of sustainable lending, continue to rely heavily on outdated, field sales rep-intensive processes. This dependency not only escalates operational costs but also undermines the borrower experience—a crucial factor in today’s competitive lending landscape. Traditional call centres, manual follow-ups, and field visits struggle to keep pace with the scale and complexity of modern lending portfolios.

At a time when lenders are under immense pressure to reduce non-performing assets (NPAs) and optimize recovery rates, collections must undergo a paradigm shift. This transformation cannot merely be about cost-cutting; it must also embrace precision, scalability, and empathy. Artificial Intelligence (AI) holds the potential to revolutionize this critical function by combining predictive capabilities with personalized borrower engagement. However, the road to AI adoption in collections is not without challenges.

AI is not a standalone solution—it is the final layer in a well-digitized, data-driven ecosystem. For collections to truly harness AI’s transformative potential, financial institutions must first digitize their processes, consolidate fragmented data, and adopt intelligent workflow systems. Only then can they unlock the full potential of AI-powered collections.

The Lending Lifecycle: A Comprehensive Overview with Data-Driven Insights

The lending lifecycle forms the foundation of credit operations, from customer acquisition to repayment. Each stage shapes profitability, risk, and borrower experience. While digitization has advanced sourcing and underwriting, collections remains a bottleneck, driving inefficiencies and higher costs. 

1. Sourcing & Lead Generation: Setting the Foundation

This stage involves identifying potential borrowers and initiating the credit relationship. Traditionally manual and time-intensive, sourcing has seen transformative improvements through digitization.

Metrics and Insights:

  • Digital-first engagement: Over 65% of loans today are sourced through digital channels, including apps, social media, and websites.
  • Cost Reduction: Institutions leveraging digital sourcing and lead generation report a 20–30% drop in acquisition costs.
  • Pre-qualification efficiency: Automated credit scoring reduces pre-qualification time by up to 50%, improving lead conversion rates.

Key Digitization Strategies:

  • AI-Driven Targeting: Lenders use analytics to identify high-potential customers, focusing on income trends, financial behavior, and demographic insights.
  • Omnichannel Presence: Integrated marketing across digital platforms improves lead visibility and customer access.
  • Instant Pre-Approvals: Automated tools powered by AI process loan pre-qualifications within minutes, enabling faster decision-making.

2. Underwriting & Approval: Assessing Creditworthiness

Underwriting evaluates a borrower’s ability to repay, making it a critical determinant of loan viability. With increasing data availability, this stage has embraced digital tools to improve accuracy and inclusivity.

Metrics and Insights:

  • Alternate Data Utilization: Lenders using non-traditional data like utility payments and spending patterns report up to 15% increase in loan approval rates for thin-file borrowers.
  • Speed of Approvals: Digitized underwriting reduces loan approval timelines from 5–7 days to as little as 2–3 hours.
  • Operational Costs: Automation in underwriting decreases costs by approximately 25%, enhancing scalability.

Key Digitization Strategies:

  • Automated Credit Models: AI systems integrate credit bureau scores with alternate data to generate accurate risk profiles.
  • Real-Time Decisioning: Lenders achieve faster loan processing by automating underwriting workflows, reducing dependency on manual evaluations.
  • Risk Mitigation: Predictive models identify potential defaulters at the underwriting stage, reducing NPA risks by up to 10%.

3. Collections & Monitoring: Wrapping Up the Credit Process

Collections, the final stage of the lending lifecycle, ensures loan sustainability by managing repayments and addressing delinquencies. However, with over 70% of institutions relying on manual processes, collections is a critical area for improvement.

Metrics and Insights:

  • High Operational Costs: Manual recovery efforts cost lenders 30–40% more per account than automated systems.
  • NPA Rates: Non-performing assets (NPAs) in India reached ₹12 trillion ($145 billion) in 2024, underscoring the need for better collections strategies.
  • Inefficiency in Recovery: Institutions using traditional methods recover an average of only 60–70% of delinquent accounts.

Key Digitization Strategies:

  • Unified Data Platforms: Integrating data from sourcing and underwriting stages gives collections teams a 360-degree borrower view.
  • Contactless Collections: Using digital channels like WhatsApp, SMS, and email reduces reliance on call centers by 25–30%.
  • Predictive Analytics: AI-powered tools identify high-risk borrowers with 85% accuracy, enabling proactive engagement.
  • Real-Time Dashboards: Monitoring repayment patterns in real-time improves decision-making and resource allocation.

Digitization Across the Lending Lifecycle: A Unified Vision

Digitization is the linchpin for creating a seamless and efficient lending lifecycle. It connects the dots across sourcing, underwriting, and collections, ensuring that every stage informs and strengthens the next.

DIGITAL LENDING LIFE-CYCLE

Current Landscape:

  • 90% digitized in sourcing and underwriting: The majority of lenders have embraced digital tools for customer acquisition and credit evaluation.

  • <30% digitized in collections: Collections remains the most under-digitized phase, limiting its ability to adapt to the complexities of modern lending portfolios.

Impact of Full Lifecycle Digitization:

  • Efficiency Gains: Digitized processes reduce operational overheads by up to 40%.

  • Enhanced Recovery Rates: Institutions using digitized collections recover an additional 15% of overdue accounts compared to traditional methods.

  • Borrower Experience: Personalized, non-intrusive communication improves borrower trust and engagement, reducing churn rates.

By bridging the digitization gap in collections, lenders can build a cohesive ecosystem that not only improves financial outcomes but also fosters stronger borrower relationships.

Challenges in Collections: Bridging the Gaps in Debt Recovery

As lending portfolios grow and borrower expectations evolve, collections—the cornerstone of sustainable lending—remains fraught with inefficiencies. Despite advancements in other stages of the lending lifecycle, collections lags behind, burdened by traditional methods that fail to meet the demands of today’s complex credit environment. Here are the key challenges plaguing collections and why addressing them is crucial for financial institutions.

Challenges in Collections- Bridging the Gaps in Debt Recovery

The Borrower Repayment Behavior Cycle highlights the stages through which accounts deteriorate, from timely repayments to eventual classification as non-performing assets (NPAs). This progression underpins the key challenges faced by financial institutions in managing collections effectively. Without timely interventions, accounts can rapidly escalate through these stages, amplifying operational inefficiencies, increasing costs, and elevating portfolio risks.

1. Fragmented Data Ecosystems

Collections teams often struggle with disjointed data spread across multiple systems, creating blind spots in borrower profiling and repayment behavior. Without a unified platform, institutions find it challenging to:

  • Accurately segment borrowers based on risk.
  • Develop targeted engagement strategies.
  • Leverage insights from sourcing and underwriting to inform collections.

This lack of integration hinders institutions from adopting a proactive approach to managing delinquencies, leaving valuable insights untapped.

2. High Cost-to-Collect Ratios

Traditional collections methods—such as field visits, call centers, and manual follow-ups—remain resource-intensive and costly. Industry data reveals that:

  • Manual recovery processes cost 30–40% more per account compared to digitized approaches.
  • High operational costs erode profitability, making collections unsustainable at scale.

For lenders, the rising cost-to-income ratio underscores the urgency of adopting cost-efficient, technology-driven alternatives.

3. Limited Understanding of Borrower Behavior

One-size-fits-all strategies often fail in today’s nuanced lending environment. Borrowers fall into distinct categories, such as:

  • Circumstantial Defaulters: Temporarily unable to pay due to financial hardships, requiring empathetic engagement.
  • Willful Defaulters: Intentionally avoiding repayments, demanding a more assertive approach.

Without advanced analytics, collections teams struggle to identify these distinctions, resulting in misaligned strategies that hinder recovery rates.

4. Regulatory and Compliance Risks

Increased regulatory scrutiny demands that collections processes comply with strict guidelines for borrower engagement and data privacy. Violations not only incur financial penalties but also damage institutional reputations. Key risks include:

  • Mishandling borrower data.
  • Overly aggressive recovery tactics that breach fair collection practices.
  • Lack of monitoring systems to ensure compliance in real-time.

Lenders must strike a delicate balance between effective collections and regulatory adherence.

The Numbers That Tell the Story

  • Global NPA Challenge: Non-performing assets (NPAs) are a trillion-dollar problem, with traditional methods failing to deliver sustainable recovery rates.
  • Inefficiency in Recovery: Manual methods recover only 60–70% of delinquent accounts, significantly lower than institutions leveraging digital tools.
  • Cost of Collections: Field visits and manual interventions account for 50–70% of total collections costs, making them financially unsustainable.

How AI Revolutionizes Collections: Smarter, More Effective Recovery

The introduction of AI into collections marks a turning point for the lending industry. By leveraging advanced analytics, automation, and empathy-driven communication, AI transforms collections into a proactive, efficient, and borrower-centric process. Institutions that have adopted AI-driven collections strategies have reported up to a 15% reduction in NPAs globally, underscoring its impact.

Key Focus Areas for AI-Powered Collections

  1. Prediction + Prevention: Predictive models identify high-risk borrowers with around 85% accuracy, enabling early interventions.
  2. Personalized Strategies: Hyper-personalization boosts recovery rates by aligning strategies with borrower needs.
  3. Sentiment Analysis: Empathetic communication improves negotiation outcomes and borrower trust.
  4. Dynamic Workflow Management: Intelligent resource allocation reduces recovery costs by up to 30%.
  5. Chatbots: Automation tools streamline routine interactions, enhancing scalability and borrower engagement.

This visual framework captures the essence of AI in collections, aligning intelligence, empathy, and precision:

HOW AI REVOLUTIONIZES COLLECTIONS

The evolution of collections reflects the broader transformation underway in the BFS ecosystem. Platforms like the Bharat Fintech Summit 2025 provide the perfect opportunity to explore these shifts. The summit’s agenda highlights how AI and digital-first strategies are reshaping lending, with a particular focus on underserved markets and tailored solutions.

Why Attend?

• Learn how AI-powered collections align with the larger digital lending strategy.

• Discover live case studies and practical frameworks for scaling collections with precision.

• Gain insights from industry experts on tackling challenges like NPAs, compliance, and borrower engagement.

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