I overheard a conversation last week in which the question was asked, “Are you using AI [artificial intelligence] in your business?”
A fair question, given it was posed to a director of a technology company. However, when the answer came back that they were, but that the focus was on machine learning (ML), the response took me by surprise.
The uninitiated party to this conversation was disappointed with the answer and the retort was along the lines that AI was where it was at and, if you wanted to stay in the game, you had to be using AI.
This customisation will revolutionise mortgage lending
It reminded me somewhat of the conversations from a few years back when ‘API’ (application programming interface) was the new buzz acronym being bandied around as the solution to anything and everything in the tech world… or, at least, by those not in it.
In fact, AI is not as ‘new’ as the sensationalised news headlines would have you believe. The term ‘AI’ was coined in a workshop at Dartmouth College in the US during the summer of 1956, with the British government being one of the early funders into its development.
Its origins even pre-date this, notably in the game of chess with computer science pioneered by Alan Turing himself a few years earlier, whereby they pitted a machine against humans.
Our industry has put machine learning to good use already
Chess tournaments still take place today with AI remaining unbeaten against grandmaster world champions.
Outcome dependent
But I digress — because, as impressive as AI is (going back to that overheard conversation), ML is not its ‘poor relation’. In fact, it entirely depends on what outcome you are looking to achieve. ML is a subset of AI, with the main difference being that AI is being trained to think for itself whereas ML uses statistical methods to allow machines to improve their results.
Great examples of ML being employed to make the user experience better are all around us in our daily life; for instance, the spam filters in email looking for word sequences and patterns as well as sender details that might trigger the filter.
This technology will not only improve the efficiency of lending processes but enhance risk assessment, customer service and regulatory compliance
Predictive text too… I’m sure we all have a story or two to tell about our experiences with that.
Everything, from the recommendations on your Netflix account to the facial recognition on your phone, is down to ML.
Our industry has put ML to good use already with applications such as document verification and the identification of fraud, but we’re only at the beginning of harnessing its potential. The one thing ML needs is data and we have a lot of that! Predicting the outcomes of mortgage applications and getting to offers faster is already in the offing, but what else can we do?
Perhaps the question is not what ML can do but what we want it to do. It’s time to think outside the box.
AI is not as ‘new’ as the sensationalised news headlines would have you believe
In the not-too-distant future, ML will be used for enhanced credit risk assessments. These models will incorporate an even broader array of data sources, including real-time financial data, transaction history and socio-economic factors. This will result in even more accurate risk assessments, reducing financial defaults and losses for lenders.
ML will enable lenders to offer highly personalised loan products. These will be tailored not only to a borrower’s credit profile but to their financial goals and preferences. This level of customisation will revolutionise mortgage lending as we know it because the product is bespoke to the customer, utilising criteria and affordability data to design individual solutions.
The one thing ML needs is data and we have a lot of that!
The industry will witness a profound transformation driven by ML. This technology will not only improve the efficiency of lending processes but enhance risk assessment, customer service and regulatory compliance.
As ML algorithms become more sophisticated and data sources more extensive, the mortgage industry will continue to adapt and innovate to meet the evolving needs of borrowers and lenders alike.
The question on everyone’s lips will be, ‘When will ML be able to predict swap rates?’
Nicola Firth is founder and chief executive of Knowledge Bank
This article featured in the October 2023 edition of MS.
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