How Agentic AI is reshaping mortgage fulfilment end-to-end

I was having an interesting debate the other day about how AI will impact banking longer-term. The most interesting angle was that AI will provide knowledge and blockchain (or distributed ledgers, if you prefer) will provide provenance, and that the two combined are a killer partnership.

Thinking about it, this gets interesting. So AI can start to inform you more and more about your financial options and choices. Take the mortgage process. You can ask any of the AI engines to come up with the best options. Equally, the AI engines can gather all the data the mortgage provider needs from you and fill in all the forms online in seconds. No more name, age, address details needed from you as it is all done automatically.

This level of automation is fast emerging as companies integrate agentic AI into their operations. In the US, for example, NEXA Mortgage is implementing agentic AI to eliminate mundane tasks and provide real-time updates. The UK’s Metro Bank is embracing similar tools, while Australia’s Lendi Group has launched an agentic AI assistant to help users monitor changes and easily search for the best deals.

Take this a step further. Agentic AI engines can not only search for options but also provide all the income details needed if you give them permission, and in a short space of time you get the approvals. Then we bring in distributed ledger technologies (DLT).

For a long while, there has been discussions of DLT providing proof in the land registry. In fact, one of the first use cases for blockchain was land registry. This is where it gets interesting as DLT could be used for recording the deeds of properties, the transfer of properties and the digital identity record of ownership. All of this being stored on a ledger that is immutable.

Again, it’s a future that’s being realised at speed, with Saudi Arabia recently launching the first national-scale infrastructure for blockchain-based property ownership transfer, and the Hungarian National Bank unveiling a DLT platform for mortgage loan data sharing.

So then, we have two technology innovations – AI and DLT – that can transform the mortgage and property process. Why isn’t this happening already?

Agentic AI & DLT: The Great Convergence

In many cases it is because the mortgage and property process is stuck in the 20th century. You have to get a real estate agent to market the property; you have to fill in lots of forms for the bank; you have to get a lawyer to manage the property transfer; and so on.

These are all last century processes and structures. In the 21st century, you should be able to do a search online using AI; find a house and view it online; make an offer and get a yes or no online; if it is a yes, delegate the purchase to an agentic assistant which will use DLT to organise the house transfer. There may still be a real estate agent, bank agent and legal agent working in the background, but they are all now empowered by agentic AI.

It's the next way that the mortgage process will be reshaped by AI. Traditional models will struggle to keep pace as agentic AI enables lenders to predict rather than react, with new kinds of intelligence.

An era of agents dealing with agents through AI that, when committed, completes with an immutable record on a distributed ledger system.

This is where the world changes.

Building the Agentic Mortgage Fulfilment Era

But building this new world – and bringing the mortgage and property process into the 21^st century – requires end-to-end action. It’s here where partners can play a role. For example, I’ve recently been introduced to WNS, part of Capgemini,  who are working with lenders to operationalise agentic AI across mortgage operations and modernise end-to-end workflow.

All of which helps accelerate the arrival at a new system where form-filling and telephones are no longer needed. You just kick off the process and it is all managed by agents dealing with agents through AI, but the key is that AI also gives you augmentation of information and DLT gives you proof and provenance.

It will be interesting to see how companies approach this space – and the right balance between technology and human input will be key. AI can handle the heavy lifting, while people can focus on the innately human aspects of the process. This ensures that technology amplifies human capability rather than replaces it.

This idea is still years away – most of us could not imagine buying a house without seeing it in reality – but give it time, it is feasible. What do you think?