Exploring the use of AI and Machine Learning for Mortgage Recommendations.
MortgageTech & API’s:
The year of the mortgage API was not to be in 2020. Lenders buddied up with selected partners, helping their biggest and most profitable accounts release API solutions via their own bespoke systems. The door was closed for everyone else. That is not how we brew MortgageTech innovation. Now, deep into 2021, we still await signs of a more open and transparent API integration with all major banks and building societies on the mortgage front.
The frustration waiting around for API’s made me focus on other solutions for MortgageTech. What could I work on with my self-taught knowledge of Python and Full Stack development skills in the meantime? Something that is not heavily reliant on lender integration that is. AI and machine learning was the answer.
The Project: AI and Machine Learning for Mortgage Recommendations.
Firstly, I wanted to design and build a website from the ground up using Python. Python is a high level programming language capable of machine learning and artificial intelligence. My main website currently relies too much on third party services. Wordpress for example, its a great blogging site but it’s simply not flexible enough to integrate more powerful code.
So I built SwitchMortgageRates.com
The primary function right now is a lender focused API — but with a difference. The API will retry different searches if the first one fails, scanning for specific parameters within the mortgage product information. It is a Python based solution to a problem: Mortgage products are currently poorly categorised by mortgage lenders and by Twenty7Tech. T7T are currently the only API sourcing solution for mortgage products across the entire industry — which I find bizarre now in 2021!
The API is part one of the project which is now complete. SwitchMortgageRates.com works well to access and retrieve product transfer mortgage deals for existing customers, that is, when the product information is provided.
Part two (under development) will be testing the input from the customer to recognise and understand their goals now and in the future. It will allow customers themselves to test various mortgage and property scenarios using AI.
Here’s an example:
Customer A: We are looking to switch rates but might possibly move in less than 2 years time when we are planning to get married and start a family. What is the best option for us now?
AI will kick in. It will analyse their current interest rate against the proposed best new deals right now, the initial rate periods, and the customer’s plans for the future. Based on these conditions, and knowing all of the costs involved now and in the future, the recommendation will account for this scenario. Early Repayment charges will be factored in along with upfront fees and other costs. I’m even looking at involving standard variable rate analysis from figures provided by the Bank of England.
It’s true to say that the regulator will have their own view on any potential AI capability — which is why I am testing this privately for now. So, before we get too carried away, it might be many years before this type of technology can be implemented. It may also need to be released with a big disclaimer but the concept is exciting. The machine learning part will kick in if the customer decides to save their profile. The system will remember the customer’s intentional plans and learn to adapt if these plans change, therefore also updating the AI.
For now though I’m just pleased with the result of my latest project. My first full stack Python website with some great tools for customers looking to switch mortgage rates now or in the future. The foundations are in place, ready to take on the future of AI, Machine Learning and of course MortgageTech - when the banks and building societies decide to fully open up their mortgage API’s.
