We are all familiar with how well data and machine learning can predict what we are thinking of buying.
By analysing our online activity – specifically keystrokes and browsing habits – organisations such as Google can anticipate our likely purchasing activity. If I loiter on a car dealer’s site long enough, my ‘feed’ will start to reflect this, and car adverts and editorial devoted to buying cars will start to appear.
We all generate digital footprints via our internet activity that can be used to second guess where we are looking to spend our money. Each day of smartphone use results in another tranche of keystrokes that further finesses my feed.
The potential for this is huge, given the day-by-day accumulation of data. Those involved in this industry say it won’t be long before an algorithm will know what you want to purchase before you (consciously) do. In a not-so-distant future, we could see organisations tracking your eye movements across the screen to deduce what most interests you.
Keep this in your mind, and now consider the sort of marketing you get from the large established lenders.
Not often do we see examples of marketing activity from the lending giants that is bespoke to our individual circumstances, even though they’re sitting on plenty of data. I can’t be the only one to be surprised at the number of ‘scattergun’ pitches I still get from my bank; many for products I have no intention of wanting.
Surely there is the scope to provide a bespoke pitch; one that reflects my specific circumstances. Certainly the quantity of data out there allows for a more granular approach, but there is not much evidence of this from the main lenders.
So why are they not going down this route more urgently?
For sure, this is partly down to data confidentiality and all the protocols that are in place to prevent abuse. Any company that collects user data (which is just about all financial service providers) is heavily regulated at the national and international level by a range of standards, such as the General Data Protection Regulation (GDPR).
But this is not the full story. An awful lot more could be done within the existing framework, providing the necessary safeguards and authorisations are in place.
The most telling reason for the slow take-up, I believe, is that larger lenders’ legacy systems often lack the infrastructure to accommodate big data analytics. The sheer volume of data stored puts a massive strain on the systems that they have, and many lack the advanced analytics to make sense of it in the first place. The larger the organisation, the more likely it will be advised to upgrade an existing system before implementing a more sophisticated data strategy.
The end result is that the major lenders tend to fall back on long-established practices and customer bases at the expense of growth and innovation. This is despite the fact that, by using analytics-driven strategies and tools, they could unlock the potential of their data to reduce costs and increase revenue. Businesses reported an average 8% increase in revenue and a 10% reduction in overall costs as a consequence of utilising big data, according to a survey from Barclays Bank.
While this might not be great for the shareholders of large lenders, it provides a really exciting opportunity for smaller, more nimble finance providers. They are in a great position to partner with one of the many FinTech companies we have in the UK who have the expertise to bring big data to life.
For the first time, smaller lenders and new entrants can leverage a competitive advantage over larger providers by using new technology.
Unrestricted by decision-making inertia, they can swiftly utilise plug-and-play APIs with the support of fintechs that will transform their lending. Many may already have a flavour of what can be achieved through clever use of data via the use of assisted underwriting tools such as Auto Decision Platform (ADP) by LendingMetrics, which delivers optimal lending decisions in seconds.
Neil Williams is managing director of LendingMetrics
