In the last edition, we talked about how AI is strengthening financial infrastructure by improving things like underwriting and risk detection. Today, I want to talk about what happens when AI stops just assisting and starts running parts of the business. 

Earlier, most of the talk around AI in fintech circled around better tools. We were looking at faster scoring, smarter dashboards, and ‘copilots’ for human teams. AI was like an incredibly fast analyst who never needed a chai break. 

Now, AI is moving from an assistant to an operator.  

Not everywhere and not all at once, but enough signals are aligning across industry forecasts to suggest a shift in operating models, not just tooling improvements. Multiple 2026 financial services outlook reports describe AI systems as moving from recommendation layers into execution workflows, where systems trigger actions instead of only suggesting them. 

Across banks and financial institutions, AI is increasingly being deployed to move cases forward, escalate exceptions, reconcile mismatches, generate reports, and run governed back-office workflows, not just score risk. These are execution tasks, not advisory ones. 

From better tools to operating systems 

The old way was all about ‘recommendations’. A decisioning model would spit out a score or a red flag, and a human team would review it and decide what to do next. 

But as we look toward 2026, the pattern is shifting. And fast.  

We’re seeing banks use AI to: 

• Move cases through the pipeline 

• Escalate exceptions automatically 

• Reconcile messy data mismatches 

• Generate compliance reports and trigger verifications 

• Run back-office workflows from start to finish 

Notice the language change? We’re hearing less about "AI-assisted underwriting" and more about "AI-driven decisioning" and "agentic workflows." It sounds like corporate jargon, but in day-to-day operations, it’s a massive leap.  

Why now? 

This isn’t happening in a vacuum. The sheer scale of investment shows that lenders and FinTechs alike are past the ‘experimentation’ phase. Now, we’re entering a phase where the rubber meets the road and we find out truly what the AI-native revolution can deliver.  

The numbers are staggering: 

• AI spend in financial services is projected to hit $97 billion by 2027 (up from $35 billion in 2023). 

• Generative AI alone could add $200B to $340B to global banking profits annually through sheer productivity. 

Banks are redesigning their core DNA. Once you trust an AI to accurately predict risk or fraud, the logical next step is to let it act on those predictions. Better prediction leads, inevitably, to automated execution. 

The back office is where AI delivers real value 

Customer-facing AI solutions like chatbots get most of the attention because customers can see it. But back-office AI is where organisations see measurable results, so that is where budgets usually go. 

Operations teams are judged on turnaround time, queue backlog, and whether policies are followed correctly. This is exactly where task-executing AI makes the biggest difference. Work such as KYC reviews, document checks, and reconciliation is well suited for AI because it follows clear rules, fixed steps, and recorded audits. 

AI can handle large volumes of routine work such as verifying IDs, spotting data mismatches, and sending files to the right queue. Simple and clean cases move ahead automatically. Unusual or complex cases are sent to a human for review. 
This split helps banks grow their operations faster while keeping control and oversight. 

How the risk landscape is changing 

Execution is a game-changer for fraud. AI already analyses millions of transactions in real-time, but now it is doing more than just flagging a suspicious transfer. It is stopping it. 

By pulling these controls forward into the onboarding process, losses go down and legitimate customers do not get stuck in fraud-detection loops as often. 

Underwriting is seeing a similar evolution: 

• Approval rates have jumped by 18% to 32% for some lenders. 

• Bad debt dropped by over 50% in specific cases. 

When AI can assess and progress a case, clean applications move forward right away. 

What 2026 looks like 

The forecast for next year is all about execution-capable agents. We are talking about: 

• Autonomous agents: AI that can gather documents, initiate workflows, and finish multi-step processes. 

• Measurable trust: Since AI is doing the work, institutions need verifiable decision records and clear step-by-step visibility into every AI action. 

• Unstructured data widely used: AI is finally getting good at reading the messy 80% of data, such as emails, pdfs, and images, and using it to make real-time loan decisions. 

The takeaway 

The conversation around AI in credit is moving beyond productivity gains to real operating model change. 

As AI systems become capable of executing tasks on their own, lenders need to rethink where human judgment is essential and where machines can take the lead. The opportunity is not just faster decisions, but stronger systems overall. Execution-ready AI will shape the next generation of credit operations. 

What do you think? I will see you in the next one.  

Cheers,  

Srijan Nagar
Co-founder
FinBox