For a long time, AI played a supporting role in finance. It analysed data, flagged patterns, and helped teams move faster. Useful, yes. Central to how decisions were made? Not really. 

That’s beginning to change. AI is moving from the sidelines and into the spotlight. Now, it’s being built directly into the flow of financial actions themselves. You can see it in payments already, where conversations move straight into transactions without the usual layers in between. 

In simple terms, the system is no longer just advising on what to do next. It’s carrying out the next step. 

Conversation is turning into execution 
At the India AI Impact Summit, Mastercard demonstrated an agentic commerce flow where an AI agent searched for a product, verified the merchant, and completed the payment autonomously. 

The gap between asking and executing is getting smaller. Financial interactions are beginning to feel less like a journey through screens and more like a conversation that moves things forward.  

Scale makes this harder to ignore 
This isn’t a one off. Agentic transactions are already happening at a significant volume. Alipay recently reported more than 120 million AI-agent transactions within a single week. 

At that level, this stops looking like experimentation and starts looking like  infrastructure. Developers begin building with the assumption that AI agents will initiate requests. Identity, consent, and governance layers move closer to the centre of the stack. Payments are showing the early shape of this shift. Lending will encounter a more complex version of it.  

Lending steps into a different kind of change 
Credit decisions carry more weight than a simple payment. Underwriting models, compliance rules, pricing logic, and long-term risk all sit behind a single approval. 

As AI agents become part of how people explore financial options, lenders may encounter borrowers long before a traditional application begins. Someone might ask an AI assistant about loan options, compare offers, and trigger a request without ever opening a lender’s website. 

This raises practical questions for the industry: 

• How should underwriting systems respond when a request originates from an AI assistant instead of a form? 

  
  

• How do lenders retain visibility into decisions when the interaction doesn’t happen inside their app? 

  
  

• If AI agents shortlist products for users, how do lenders ensure their offerings are represented accurately? 

 
A year ago these may have been theoretical debates. Today, they are very real  product and infrastructure decisions. 

Decision infrastructure moves closer to the surface 
Financial products used to be about smooth, flashy interfaces. Screens, journeys, and user flows shaped how people experienced lending. 

As AI becomes part of the interaction layer, more attention shifts toward how decisions are structured behind the scenes. Underwriting logic needs to run in real time. Policy rules need to be accessible beyond a single channel. Transparency has to be built into the system itself. 

The focus moves closer to the mechanics of decisioning rather than the polish of the interface. 

Bringing credit decisions into AI workflows 
This context shaped our recent update to Sentinel AI. With MCP compatibility, credit decisions can now be accessed directly within AI conversations, allowing underwriting and approval workflows to operate inside the environments where financial interactions increasingly begin. 

The intention was straightforward: ensure that lending infrastructure remains usable as interaction models evolve. AI environments require clear guardrails, transparent decision paths, and strong governance. MCP provides a structured way for lenders to expose decision systems while maintaining control. 

For lenders, this opens new territory around distribution, orchestration, and how credit decisions reach borrowers. 
 

 
What this means for the ecosystem around lending 
As agentic finance develops, several shifts begin to take shape. Technology platforms will likely invest more heavily in orchestration layers that manage consent, identity, and auditability. 
Data infrastructure will need to support real-time decision calls rather than delayed workflows. 
Product design may begin earlier in the stack, at the level of decision logic rather than interface design. 
 
Some organisations will experiment early. Others will take a more measured path. Both approaches are understandable given the pace of change and the regulatory context lenders operate within. 

Looking ahead 
Payments are giving the industry an early look at how AI-driven execution might work. Lending will move more cautiously, but the direction is becoming clearer. 

As AI starts participating directly in financial actions, the question for lenders shifts from “What does our interface look like?” to “How does our decision system operate wherever the borrower shows up?” 

The tools are changing roles. And that changes where the centre of gravity sits inside modern financial products. 

Cheers,  

Srijan Nagar
Co-founder
FinBox