Part One the Problem:

The era of the intent-based chatbot is over. The days of sitting in front of an IBM Watson-style bot, waiting for a human agent to reply and tell you whether you qualify for a loan or an overdraft 90 minutes later are gone. Users are now expecting instant answers. ChatGPT, Claude, and Gemini have shown them how to get accurate responses in the blink of an eye.

So are banks catching up with generative AI and agentic AI?

Unfortunately, not really. Four years after the launch of ChatGPT, users are still struggling to get straight answers out of their banking chatbots.

What people actually ask

I've worked at one of the largest bank in UK and analysed millions of conversations across lending and credit: loans, mortgages, credit cards, overdrafts, and more. The questions people ask fall into five recurring categories:

1. General product information

• What is an overdraft?
• What's the difference between an overdraft and a loan?
• What is the daily charges for an overdraft?

2. Personalised questions

• How much did I spend on groceries last week?
• How much is my loan or overdraft actually costing me?
• Can I get a copy of my bank statement?
• Can you recommend a product for me?

3. Applications

• Can I get a loan?
• Can I apply for an overdraft?
• Can I get a credit cards?
• Can you increase my overdraft limit?

4. Advice

• Between a loan and an overdraft, which suits my situation better?
• Can I get a loan to pay off my overdraft?

5. Financial hardship

• I'm in a difficult situation, can you lend me money?
• I'm struggling to pay my overdraft because of X, can you pause the interest for a short period?

How quickly and accurately you answer these questions is the quality of service you're offering. And that's what will drive revenue going forward. Imagine walking up to your bank's chatbot, asking for a credit card or an overdraft, and getting a decision in 5–10 minutes not hours that will be amazing.

The real bottleneck isn't "agentic AI"

Here's the thing: answering those questions well doesn't require an "agent" in the fashionable sense. What it requires first is a rich context layer that actually knows the customer and their history.

• When a customer opens the chat, do we know their transaction history?
• Do we know which products they're already subscribed to?
• Do we have their credit file?
• Do we know how affordable a new product would be for them?

Is all of that sitting in one place, giving a true 360-degree view of the customer? And if your bank is part of a group, do you have that 360 view across every entity in the group?

This isn't an agentic AI problem. It's a data engineering problem but it's the foundation everything else is built on.

Next comes your product layer. Is your product documentation actually written down and accessible somewhere? How are you running affordability checks? How are you generating recommendations? Recommendation engines aren't new, they've existed for decades. Do you have one that can accurately match a product to a customer's need, at the right moment? Get this right, and you've solved a big part of the equation.

Then there's your core banking infrastructure. Is the loan application journey actually built, so a customer can apply and get an accurate answer quickly? Do you know which APR to give to which user to minimize risk? Can they request a refund on a transaction and get a response? Can they ask for an overdraft limit increase and hear back fast? And critically are all of these journeys compliant and properly regulated, ready to stand up if a regulator comes asking?

None of this is an agentic AI problem either. It's a core banking problem and most established banks handle it reasonably well already.

If the foundations: the data layer, the product knowledge, the core banking workflows are actually in place, then it's worth talking about agentic AI and everything that comes with it.
That's the gap. Not a gap in AI sophistication, but a gap in foundations. Get the data, the product logic, and the core banking journeys right, and the "agentic" layer on top becomes the easy part not the hard one.

The example in the top is not a fictional chat, it comes from a real conversation. Last month my colleague come and say he applied for a mortgage at our company, they come back to him asking him for 3 month of payslip. The same bank he work for, the same bank that send him those payslips.

The Agentic Bit

If it’s deterministic, don’t make the LLM do the work From NU Bank.

V2 extends the same core flow with three additional capabilities, each living in its own network with its own gateway:

1. Context Layer : User Personalisation

Before running its agentic workflow, the Agent Engine calls a Context API / MCP Server to fetch the user's data including their propensity to products (a signal for which financial products they're likely to be interested in or eligible for) and. product recommendation. This happens as a pre-step, ahead of any reasoning about what to say or which workflow to present. This layer is by another team that build the user product recommendation engine. Remember the agent does not decide which product to recommend to the user. It is the recommendation engine. The user come with it question and need and the agent call this function and return recommended product if the question were about product recommendation.

2. Tools / Product Documentation: RAG

To answer product-related questions accurately, the Agent Engine calls a Product Info Tool that performs retrieval-augmented generation (RAG) over documentation.

If a user ask generic question about product information about policies, this is responsible of handling that.

The rag tool lives somewhere and it regulated by another team.

3. Workflows: Loan and Overdraft Applications

The Agent Engine can present existing business workflows to the user (e.g., a loan application or an overdraft application) but does not own, execute, or drive them. These workflows are business processes owned and operated by the buisness. The Agent Engine's role is limited to:

• Calling the Workflow API to retrieve and present the relevant workflow to the user, pre-populated using the user's context data.
• Which workflow to present is decided by combining two signals: the user's profile (specifically their propensity to products) and the intent of their actual question. It is not a fixed decision tree — both signals feed into the choice.

When the user clicks a button to actually start a workflow (e.g., "Apply now"), that action calls the workflow team's API directly to execute it. The response and its content are owned by the workflow team the Agent Engine does not generate or interpret the business outcome, it only receives the result once the workflow team's system produces it (e.g., "approved," "declined"),and the reason behind those message.

But what do you think about this problem, how are the bank solving it? I have only found two banks doing this well, NUBANK in Brazil and Revolut and they barely share blog on their approach. Nu Bank and Revolut with the Pragma Paper are my favourite one..

Disclaimer: Post on my second account for privacy, I used GenerativeAI model to profread some part of this post.