How machine learning helps banks compete in a fintech world

Posted by Lee McFarland on Aug 21, 2019 12:18:04 PM

The phrase “disruptive technology” is thrown around a lot these days, but there’s no industry where its effects can be more keenly seen than banking. With the rise of digital-only fintech companies like Monzo, Iwoca, and eToro, traditional banking is being forced to step up and compete. But with fintech companies developing so rapidly, how can banks keep pace? Well, one option is machine learning, or ML. Here are just a few ways that machine learning can help banks stay relevant. 


Personal security and fraud prevention


One of the biggest benefits of machine learning is its ability to identify trends in user behaviour. This means that tracking breaches such as financial fraud becomes much easier. Historically, a lot of the work done by fraud prevention teams utilised at least some manual processes, but machine learning makes all this frictionless. The advanced algorithms made possible by ML make the process of spotting anomalies and potential frauds completely automated. For banks, this means they’re able to ensure higher personal security for their customers. 


Lending decisions and risk assessment


Traditional banking institutions are well known for making lending decisions on a person-to-person basis. This means that human intervention is often required to assess the risk and check affordability, even with credit scoring in place. But this solution isn’t scalable, and – perhaps even worse – it introduces an element of subjectivity or human error. With machine learning, all of these decisions can be left up to sophisticated AIs equipped with up-to-the-second information about the spending habits and credit history of an individual.


A more personalised service


A key aspect of machine learning is that it’s able to monitor and track the trends of specific users or groups of users. This means that a banking tool based on artificial intelligence will be able to predict -- with uncanny accuracy -- the specific service a person may need next. This level of insight allows for advanced automation, targeted offer placement, and an overall more personalised service for the bank customer. Machine learning can even track the price sensitivity of a person to inform product offer decisions. 

It’s clear that traditional banks are slowly catching up to the new breed of fintech companies. But for how long? That remains to be seen. To discover more about how machine learning could help bring your business into the AI-powered future, get in touch with the Aqovia team today.

Topics: Artificial Intelligence, AI, Technology, Digitalisation, Transformation