Hi,  

 
If banking partnerships are the new rails for credit, AI is the engine and the steering that determines how far and fast you go. 

Here’s what I’m seeing on the ground: The partnership lending sector is thriving right now, banks and NBFCs are meeting borrowers on apps, QR platforms, and commercial marketplaces. But speed without control is just a faster way to slide. Last week, I watched one lender push the same credit rule into three partner flows and get three different behaviours. That’s a problem with translation. We write credit policy once and expect it to function similarly on five different platforms. It won’t. Some things in life benefit from standardization, but lending is just not the place to aim for it. 

Partnership lending is the solution to the age-old problem of effective and contextual distribution. However, it’s the operational bits that remain unsolved. Even in digital partnerships, too much of lending is still manual, rigid and runs on standard policies. Blimey. 

Why does partnership lending need AI? 

The surge in credit offtake has been impressive. But equally disappointing has been the inertia of participants to digitally transform the plumbing that runs this world. Let’s look at some data.  

In FY25, fintech NBFCs sanctioned a whopping 10.9 crore personal loans worth ₹1.06 lakh crore, with much of the distribution happening through apps and partner pipes, not physical branches. “Credit on UPI” is currently clocking around ₹10,000 crore a month, with ₹100–200 crore from the new credit-line rails and the rest via RuPay credit cards linked to UPI. Partner apps have become lending pipes of their own. Paytm disbursed ₹4,315 crore of merchant loans during Q4 FY25. 

PhonePe’s Sameer Nigam describes their role bluntly: a massive payments platform and “increasingly, financial services distribution as well.”  

This is the good part. A sunrise industry with massive potential for growth.  

However, without the right tooling – the dividend does turn into a debt. And fast.  

Manual translation of code and inefficient builds still slow integrations, one-size policies ignore how each partner (and their borrower pools) behaves, and compliance still slips through the seams, must-do steps in the wrong order, disclosures too easy to miss. One could go on, but this is no place to be pessimistic. 

The crux is that while credit demand has moved to platforms, the execution is broken.  

 So, how can AI transform the partnership credit value chain? 

Here’s the shift I expect to see, not five years out, but quarter by quarter if we wire it right. 

 Automated partner onboarding and policy translation  

Most of the delay in partnerships is translation, not technology. AI agents can help read a lender’s rulebook once and compile it into partner-ready assets—OpenAPI specs, field mappings, validation rules, and test cases—so integrations come together in days, not months.   

 Dynamic credit policy optimization 

One policy everywhere is blunt. Different partner cohorts (e.g., marketplace BNPL vs. consumer durables co-lending) need bounded tuning. ML models can analyze performance by segment and propose small, auditable adjustments to cut-offs, documentation asks, and pricing bands. Policies can continuously recalibrate against portfolio outcomes instead of waiting for quarterly rewrites. 

As the embedded/BNPL rails expand, per-partner tuning using AI can stop lenders from over-guarding the easy cohorts and under-guarding the tricky ones. 

 Embedded compliance and guardrails 

Most issues start in the seams, including sequence, disclosure, and scope. AI turns intentions into enforcement: pre-flight checks can block mis-sequenced journeys before release; runtime monitors scan for early warnings of mis-selling, data-scope creep, or even partner fraud. Evidence packs (what ran, in what order, for whom, and why) generate automatically, making audits procedural instead of painful. 

 “Compliance by design” can ensure scale without the regulatory headaches. 

Personalized borrower journeys across partners 

Journeys should flex to context while the risk spine stays identical. NLP-based underwriting and behavioural scoring let the system ask only what’s needed, when it’s needed, then surface the right offer path for that partner and cohort. The result is consistent CX across very different platforms, lower friction where trust is earned, more evidence where risk is uncertain, without fragmenting policy or compliance. 

 What changes for the lenders?  

First, lenders get speed they can sustain. AI pulls us out of retyping and reconciliations, so launches move in weeks instead of months, and they move with guardrails. We compile the policy once, it travels everywhere, and ops stops firefighting while Risk stops rewriting the same rule five times.  

Second, you start running a portfolio of partners, not a pile of integrations. A gated ecosystem, a mid-size fintech, a niche platform, shouldn’t all be treated the same. With AI, you get to set appetites per partner (protect loss, push approval, hold margin), let tight, bounded tweaks do the work, and watch performance at a portfolio level.  

Finally, AI helps in building resilience by design. Plug-and-play becomes real when compliance ships with the code. New partner? Drop in the same journey sequence and watch drift with live telemetry,  and produce evidence packs on demand. You scale without surprises, and when the market shifts, you switch lanes without disassembling the chassis and rebuilding the car. 

 The FinBox PoV 

At FinBox, we spend a lot of our time rethinking the ways in which lending can be transformed. With the evolution of channels, products and even underwriting – we believe that the next frontier to push is orchestration.  

Orchestration is where most mistakes are made. It’s the dark and musty outhouse nobody wants to go clean up.  

Ultimately, policies should travel as dynamic workflows, not PDF. So we built the stack to do exactly that: FinBox Prism compiles your rulebook into partner-ready interfaces (the plumbing that makes soft-pull, consent, and eligibility feel plug-in); Sentinel keeps the rules alive and testable, so you can tune within guardrails, promote what works, and explain every outcome. Journey Builder holds the order honest across surfaces and the LOS gives you the telemetry and audit trail in one place.   

This is the posture I recommend: ship guardrails with the release, tune at partner level within bounds, let learning propagate across funnels, and keep explanations and explainability attached to every change. When policy moves like code, adding a partner should feel like a breeze, not a 6-month project for developers. That's the shift we’re committed to enabling. If you’re looking to learn more about how we view partnership lending and how the best in the industry approach it, I highly recommend reading our recent ebook on the subject. At the same time, if you’d like to talk more specifics and see if we can help architect a winning partnerships playbook for you – reach out to our experts for a live walkthrough of our cutting-edge lending products. We’ll show the orchestration layer end-to-end, how policy becomes runtime, how guardrails travel, and how you scale partners without surprises. 

This is all for this edition. I will see you in a fortnight.  

Cheers,  

Srijan Nagar  

Co-founder  
 
FinBox