Eighteen months ago, ‘agentic AI in lending’ was mostly conference chatter. Today, 52% of financial firms are running agentic AI in live production, according to a global survey.
What makes this shift fascinating is how the traditional power dynamic has inverted. Normally the largest, best-funded banks set the pace and everyone else follows years later. This time, firms in emerging markets are reporting more mature agentic AI deployments than firms in wealthier economies. This means the fastest movers are the ones with the smallest budgets and the least room to get it wrong.
From static rules to agentic systems
Most loan origination software is built on strict, predictable logic: if an applicant meets condition A, move to condition B; if not, flag the file for a human review.
An agentic system works far more dynamically. It reads through the application context, weighs the available data, and determines its own next move—deciding which records to pull, when an exception requires human review, and when a file can be approved outright.
The decision-making shifts directly from the person who designed the workflow to the system running it. And that’s why this technology requires hard safeguards: human-in-the-loop controls for high-stakes decisions and explicit regulatory rules that define the system's boundaries
Compliance is not an afterthought
Regulators aren't waiting for this technology to settle before stepping in. In April, US banking regulators issued new guidance on how banks should manage AI risk. A month later, India's central bank rewrote its rules for digital lending. Both moves send the same message: the rules are arriving at the same speed as the technology, instead of years later.
Granting AI systems greater operational freedom demands higher discipline, not less. As an agentic engine gains the ability to pull data and act independently, three controls become non-negotiable:
- Auditability: A clear record of every decision the system makes.
- Clarity: Plain-language explanations attached to every outcome.
- Oversight: Mandatory human sign-off above specific risk or credit thresholds.
Far from creating friction, these guardrails provide the safety boundaries a bank needs to scale agentic lending with confidence. For banks building agentic decisioning properly, with a clear record of every decision and a defined point where a human signs off, this is simply the cost of doing it right.
Whether it's a large bank running its own model or a smaller lender running a decisioning layer on top of an existing system, the same standard applies: every decision needs to be explainable, and every high-stakes call needs a human who can actually stop it.
Scale decides who can absorb the risk
A large global bank can afford an early mistake. It has the compliance team, the security budget, and the room to fix things before anyone notices. Most banks don't have any of that, and they're expected to move at the same pace regardless.
This gap is widest in emerging markets; Sri Lanka is a good example. A country now home to more than 160 fintech ventures, most operating on tight budgets in an economy still finding its footing. A full, ground-up replacement of a bank's core system takes years and tens of millions of dollars — money and time most institutions simply don't have. If adopting agentic lending meant replacing legacy infrastructure first, most of the industry would be left behind.
A layer, not a complete rip-and-replace
The practical path for most banks isn't replacing what they run today, Instead, they can start off by adding an agentic layer on top of it. That’s why we built Sentinel, an AI-native decision management system that plugs straight into your existing lending stack rather than requiring you to rebuild from scratch.
Until recently, even the best AI-led lending journeys would eventually hit a wall: a borrower could ask an AI about a loan, get a great conversational experience, and then the AI would have to redirect them to a form or a website, because the decisioning engine underneath was never built to be called mid-conversation.
With Model Context Protocol (MCP), Sentinel eliminates that hand-off entirely. Any AI agent, whether an internal bank copilot or a third-party assistant, can query Sentinel directly to deliver a full credit decision within the ongoing conversation. The system executes credit bureau pulls, fraud checks, risk pricing, and policy-governed approvals in real time, so the interaction continues without interruption.
Sentinel also deploys a library of specialised AI agents that pass work off to one another, just like human team members would. One agent checks documents, another pulls data, another compares financials, and another generates CAM reports. While the underlying safety rules, audit trails, and approval standards remain strictly governed, AI agents now handle the background work leading up to the final decision.
Overlaying an intelligent decisioning layer onto existing infrastructure provides a direct, high-speed route to agentic capability. The rapid adoption in emerging markets demonstrates that a multi-year, ground-up rebuild and modernisation were never the mandatory prerequisites the industry assumed.
Institutions can deploy autonomous decisioning immediately without waiting to replace their underlying core.
That said, this approach buys time, not a permanent pass. I have made this case before: leave the core unmodernised indefinitely, and the technical debt underneath keeps compounding, no matter how capable the layer on top looks. The banks navigating this efficiently execute a dual-track strategy: deploying intelligent overlays to generate immediate value today, while modernising their core systems at a steady, sustainable pace in the background.
What changes for lenders
For decades, superior lending came down to two things: sharp underwriters and deep historical data. That's still true, but it's no longer the whole answer. A credit policy written once and left untouched rarely maintains its peak performance for long.
Research on credit risk models puts this plainly: static models measurably lose accuracy over time as borrower behaviour shifts, while models built to continuously retrain against new outcomes actually improve. One study testing this on a small business loan portfolio found a nearly 6% improvement in model accuracy, catching almost 10% more defaulters early, after twelve months of continuous updates.
This marks a fundamental shift in how lending infrastructure must be designed. Operating a system that monitors its own outcomes and adjusts dynamically creates a permanent guardrail against credit risk, maintaining margin quality before hidden defaults accumulate.
Moving forward, the true advantage goes to the institution whose system continues to improve every single day it operates.
Until next time,
Srijan
Co-founder
FinBox
