Partnership Lending Transforms from Side Channel to Core Credit Strategy
Partnership-led credit distribution has undergone a fundamental transformation, moving from a supplementary channel to a central strategic approach for financial institutions. Artificial intelligence is increasingly powering the operational infrastructure behind this shift, according to insights from the inaugural episode of The Credit Continuum podcast series by Yubi.
During the podcast conversation between Vipul Mahajan and Manish Gupta of L&T Finance, the discussion centered on how lenders are navigating the transition toward more embedded and automated credit systems. Gupta revealed a significant indicator of this transformation: at L&T Finance, approximately 70–80% of lending now occurs through partnership channels.
The Evolution of Embedded Credit Delivery
This strategic shift mirrors earlier transformations in payments and customer onboarding processes, where customer journeys migrated from branch-centric approaches to digital flows designed around context and convenience. In the partnership lending model, customers are not actively seeking loans but rather attempting to complete specific tasks—whether purchasing products, making payments, upgrading devices, or bridging short-term financial gaps.
Credit emerges as an integrated byproduct within these transactional moments, seamlessly embedded into customer experiences rather than presented as standalone financial products.
Three Driving Forces Behind Partnership Success
The conversation identified three primary forces propelling the partnership lending revolution:
- Distribution Migration: Credit distribution has moved to where customers already conduct their activities. Financial institutions no longer need to maintain their own storefronts when they can integrate their lending capabilities into existing platforms with established reach and customer data.
- Real-Time API Expectations: The advent of real-time application programming interfaces has fundamentally altered customer expectations. Credit offers can now be generated and fulfilled within dramatically compressed timeframes. When decision-making processes become rapid and repeatable, partnerships become significantly more scalable than traditional physical acquisition models.
- Regulatory Formalization: Partnership lending structures are becoming increasingly standardized through regulatory frameworks. After multiple cycles of scrutiny, audits, and tighter rules, the sector is entering a phase where clearer responsibilities and risk-sharing terms are becoming foundational elements.
Regulatory Developments Shaping the Landscape
The timing of regulatory evolution is particularly significant. In August 2025, the Reserve Bank of India issued revised co-lending directions that expanded the scope of co-lending arrangements beyond previous constraints. These regulations require each regulated entity in a co-lending partnership to retain at least 10% of each individual loan on its balance sheet, with the framework scheduled for implementation on January 1, 2026.
Regarding default guarantees, the central bank's digital lending rules cap default loss guarantee coverage at 5% for eligible portfolios. This limitation is increasingly viewed as a design constraint rather than a regulatory workaround, shaping how partnerships structure their risk-sharing arrangements.
Co-Lending: Incentive Alignment Beyond Capital
Mahajan framed co-lending as a maturing component within the partnership ecosystem. Gupta emphasized that clearer regulatory frameworks have reduced ambiguity in risk pricing and sharing mechanisms, pushing partnerships toward more transparent alignment structures.
The underlying logic is fundamentally structural. Larger financial institutions typically access cheaper capital, while distribution partners contribute extensive reach, deep customer context, and the capability to create seamless customer journeys. Co-lending serves as the mechanism that combines these complementary strengths, while regulations governing minimum retention and default guarantees reduce incentives to pursue scale without appropriate accountability measures.
Gupta highlighted a practical advantage of this approach: blended economics can enhance serviceability for end borrowers, particularly when lower capital costs are partially passed through to customers.
The AI Transformation: From Models to Workflow Integration
If partnerships represent the distribution layer of modern lending, artificial intelligence is increasingly positioned as the workflow layer. Gupta described the "pre and post" transformation that financial institutions have experienced since generative AI entered mainstream operations.
While earlier AI applications in credit focused primarily on improved scoring and underwriting models, contemporary implementations span across origination, funnel recovery, and collections processes. Gupta identified three primary areas where lenders are deploying AI most aggressively:
Three Key Areas of AI Deployment in Lending
Underwriting Precision: AI and machine learning models are being deployed not merely to accelerate approval processes but to identify hidden risk and safety pockets within segments that appear homogeneous under traditional analytical frameworks. This approach transforms underwriting from a single-score determination to layered decision-making processes that can safely include customers who might be excluded by default rules.
Funnel Management Optimization: Partnership funnels typically experience breakdowns for predictable reasons—failed steps, stalled document flows, verification layer drops, or mid-journey customer exits. Gupta emphasized the need for systems capable of early detection and automated response triggering. In India's diverse linguistic and cultural context, manual intervention becomes economically challenging, making automated systems essential for identifying drops, determining appropriate nudges, and communicating with borrowers in their preferred language and tone.
Collections Transformation: Collections activities are evolving from brute-force approaches to prioritization and prediction problems. Most borrowers, Gupta suggested, operate in good faith but occasionally miss payments, forget due dates, or require timely reminders. AI systems can segment borrower bases, determine which cases require human intervention, and personalize interactions to maintain compliance and appropriateness. Early warning systems that blend broader signals with product-specific patterns enable lenders to identify financial stress earlier and intervene before delinquency solidifies.
Governance: The Non-Negotiable Component
The most significant tension highlighted in the podcast episode concerned not adoption rates but implementation restraint. Mahajan identified a common failure pattern where governance frameworks are added as afterthoughts following use case scaling. Gupta responded with a clear design principle: governance must be embedded within workflows to enable early issue detection.
He highlighted three critical risks that lenders must proactively address:
- Hallucination and incorrect outputs in automated decision support systems
- Bias infiltration through data or poorly designed prompts
- Explainability gaps that prevent tracing decisions back to original inputs
From a lender's perspective, explainability represents not merely a regulatory requirement but a practical necessity for debugging systems making thousands or millions of decisions. Gupta added a frequently overlooked constraint: when AI systems communicate with borrowers—particularly in collections or nudging contexts—tone matters significantly. Empathy functions not as a marketing layer but as an integral component of risk and compliance design, since interactions themselves can shape financial outcomes.
Building Future-Ready Lending Operations
When asked about building partnership lending with AI at its core, Gupta emphasized operating models rather than technical checklists. His recommendations included:
- Cultivating Collaborative Culture: Moving beyond sequential handoffs between product, policy, technology, and operations teams to create cross-functional pods that collectively build and own customer journeys.
- Developing Nimble Systems: Building flexible architecture and strong engineering capacity to accommodate evolving partnerships, changing policies, and rapid integration cycles demanded by partners.
- Selecting Partners Strategically: In partnership-led markets, portfolio quality directly correlates with partner quality. Selection criteria should extend beyond reach to include alignment on risk approaches, data discipline standards, and customer outcome commitments.
This framework reveals the emerging contours of next-generation lending: partnerships determine where credit is delivered, AI determines how customer journeys are executed, and governance determines whether systems scale safely. The Credit Continuum podcast series positions itself as a window into this operational reality, where underwriting, funnel recovery, and borrower interactions are increasingly becoming software challenges as much as credit challenges.
