Mahindra Finance’s Central Processing Centre (CPC) has undergone a radical efficiency overhaul.
Perhaps most remarkably, CPC file volumes have surged eight-fold even as headcount fell from 885 to 465, a 47% reduction. The numbers tell a clear story: automation is absorbing incremental volumes rather than merely shifting work between teams.
The Samur.AI multi-agent system sits at the heart of this transformation. Operating within the CPC rather than the credit-decision layer, Samur.AI handles document and data verification—including regional-language documents and highly variable dealer documentation. When a case requires human review, the system provides a rationale, enabling seamless collaboration between AI and staff. Coverage has expanded aggressively, from 20% of business operations in Q4 FY26 to 45% in Q1 FY27, and now approximately 70% of new-loan volumes. InvestorPresentations +1
The causal mechanisms driving these gains are straightforward. Straight-through processing capabilities improved by 40%, meaning more applications flow through automatically without manual intervention. The system achieved 80% faster post-sanction turnaround time for the business segments where it was deployed. By standardizing processes and eliminating manual bottlenecks, Mahindra Finance has created a processing engine that scales efficiently without proportional headcount increases. InvestorPresentations +2
The Udaan digital transformation programme has shifted lending from manual, decentralized processes to a standardized, technology-driven operating model across 1,300+ branches. This rollout covered more than 1,300 branches in 11-12 months, compared to the industry standard of 2-3 years for similar implementations. The speed advantage stems from several factors: an existing technology foundation, a phased implementation strategy, centralized processing architecture, and strong executive sponsorship.
Branch productivity has surged despite manpower remaining largely flat over the past 2-3 years. Close to 50% of FY26 disbursements flowed through the Udaan digital stack, and 100% of wheels business disbursements—approximately ₹15,000 crore in Q1 FY27—now execute on the new stack. The M TEZZ field app plays a crucial role here, enabling employees to toggle between onboarding and collection modules seamlessly. By streamlining these processes, the app frees up branch staff time that can be redeployed toward proactive sales and cross-selling. Transcripts +1
The branch network is evolving from transaction processing centers to customer relationship hubs. Management has reorganized branches with brand share and cross-sell desks, optimized the network for multi-product journeys, and horizontally widened employee skillsets to enable multi-product sales. This transformation is directly supported by the productivity gains from automation—branches are moving from a service-heavy model toward outbound sales and cross-selling across insurance, fixed deposits, mutual funds, and personal loans. Transcripts +1
Mahindra Finance possesses a distinct competitive advantage: 30 years of proprietary repayment data. This historical treasure trove has been converted into sophisticated customer personas and scorecards, enabling the company to assess new-to-credit customers even without conventional bureau history. The company has built powerful machine learning analytics models extended through digital platforms for customer acquisition, collections, and forecasting.
The underwriting framework integrates multiple data sources. Customer attributes including geography, landholding, and crop mix are mapped against historical repayment behavior to generate initial offers. The MMFSL Bharat Maps proprietary data-driven financial and consumption map of India provides village-level information on economic prosperity, industry lending activity, irrigated land, healthcare centers, and post offices. This geographic intelligence enables precise location-based risk assessment across more than 12,500 PIN codes. Transcripts
For customers without documented digital footprint, the system triggers physical business assessment. This hybrid approach—digital-first for those with sufficient data, physical assessment for informal-income customers—creates a triage mechanism that balances speed with risk discipline. Around 85% of cases are processed within targeted turnaround time, with the balance requiring additional checks due to limited bureau or banking information or other exceptions. This 15% exception rate represents a deliberate design choice rather than a limitation, ensuring robust risk management for complex cases.
The impact of faster decision-making is visible in market performance. Tractor disbursements grew more than 45% in the previous year, compared to industry growth of around 20%, while Mahindra Finance’s own OEM-linked growth was approximately 22%. The company achieved 65% tractor disbursement growth in Q3 FY26—its highest ever Q3—capturing seasonal demand windows more effectively than competitors. Transcripts
Digital lending has compressed timelines dramatically. Time to first offer has declined from 6-7 days to about 20 minutes, while the broader login-to-disbursement cycle has fallen from 10-12 days to same-day or within two days for most cases. The lending workflow is supported by more than 13,000 business rules, Account Aggregator and bureau integrations, digital KYC, e-sign, and e-mandates. E-mandate penetration in newly sourced portfolios has reached 85-90%, with around 70% of the outstanding portfolio now covered.
Collections have also become increasingly digital, rising to 75-77% of total collections. This digital transformation correlates with improved portfolio quality: GS-3 (NPAs > 90 days) reduced to 3.4% in Q4 FY26, down 39 bps quarter-on-quarter, while collection efficiency is maintained in the 95-96% range. The causal link is clear—e-mandates enable automatic debit on due dates, eliminating behavioral delinquencies stemming from forgetfulness or payment timing mismatches. InvestorPresentations +2
AI is reshaping collections at scale. The company has deployed 12 AI vernacular bots for customer reminders, achieving a 25% improvement in early bucket efficiency. Through a partnership with Sarvam, Mahindra Finance has completed more than one crore calls across sales, collections, and employee engagement in 12 Indian languages. The machine learning models identify relevant customers or actions, while voice agents turn those predictions into conversations at scale. Transcripts +1
Field-force optimization has become data-driven. The company reallocates contracts based on GPS coordinates to specific employees, ensuring each handles no more than 250 customers for sharper outcomes. A “central nervous system” creates customer personas based on behavior and relays feedback quickly to ground teams. The collection process prioritizes high-value, high-probability contracts, maximizing recovery efficiency. Transcripts
The productivity gains from automation are being redeployed toward growth and cross-selling. Insurance premium reached approximately ₹950 crore in FY26, while fixed-deposit mobilization has doubled over the past two years. A roughly 500-member direct marketing team generates more than ₹200 crore of monthly business. The company also sees around 150,000-200,000 branch walk-ins monthly, providing further cross-selling opportunities.
Despite comprehensive automation, customers seeking higher loan-to-value ratios trigger additional field verification. This policy represents deliberate risk management rather than technological limitation. Higher LTV loans carry elevated risk due to reduced collateral buffers and increased susceptibility to fraud. Physical verification provides critical risk mitigation that automated systems cannot replicate.
This exception-based processing model optimizes efficiency for standard cases while maintaining risk discipline for higher-risk scenarios. Approximately 85% of cases flow through fully automated digital workflows, with 15% requiring additional physical verification based on risk indicators including higher LTV requests, limited bureau information, or informal-income profiles.
Samur.AI’s ability to process regional-language documents and variable dealer documentation expands Mahindra Finance’s addressable market in rural and semi-urban segments. By eliminating language barriers and accommodating local business practices, the company can serve customers that purely digital lenders cannot reach effectively. This multilingual capability, combined with deep physical presence across 5,00,000+ villages, creates a sustainable competitive moat.
Mahindra Finance’s digital transformation demonstrates how technology can simultaneously accelerate processing, enhance credit quality, and expand market access. The company has achieved what many considered impossible in rural finance—industrial-scale efficiency without losing the local relationship advantages that define its business model. As Samur.AI coverage expands toward 100% and the Udaan programme continues to optimize, the gap between Mahindra Finance and competitors is likely to widen further.
