With the financial world having bifurcated into establishment giants and fintech challengers, one of the key differentiators has been the use of AI. As the old guard increasingly wakes up to the reality of digital transformation, however, it too is now adopting techniques including machine learning to unlock latent efficiencies. Indeed, , across the globe AI has the potential to deliver $1trn of additional value in the banking sector every year.
The scale of that potential is down to the particularly broad range of use cases for AI in the industry, not least in customer experience, as Babak Hodjat, VP Evolutionary AI, Cognizant, explains: “One of the most important use cases for AI in banking is to improve personalisation for customers – especially for incumbents looking to compete with digital natives. This is because AI can spot novel strategies that would never have been identified by human data scientists, and, in turn, allow companies to take full advantage of today’s massive data sets – ultimately helping to provide hyper personalised experiences tailored to specific customers. For example, through the use of advanced chatbots and tailored content and interfaces in apps and on digital platforms.”
Part of enabling a good customer experience is offering a well thought out digital banking solution - yet another area in which AI is enabling change. “AI and advanced analytics can then produce the insights that will help banks anticipate a customer’s next need: how they travel through the app, what offers they respond to, and how they use each service,” says Alex Kwiatkowski, Principal Industry Consultant, Global Banking Practice at SAS. “It’s like adding GPS to the digital footprint of every customer, to see how they move through their digital world. Roll the data up to analyse major trends, and keep the data at the customer-level to figure out the next-best action.”
The final and arguably most important avenue for AI in banking is in risk detection and prevention. “Tools such as AI can help banks monitor for suspicious activity in a very efficient way,” says Hans Tesselaar, Executive Director at banking interoperability association BIAN. “It is unrealistic to think a bank would be able to successfully keep its entire network protected with manual effort, alone. Not only would it be capital intensive, but more risks would likely go unnoticed due to human error. AI can help reduce the manpower needed and can also conduct surveillance seamlessly around the clock.”
The technology is particularly adept at spotting instances of fraud in unobtrusive ways by picking up on small giveaway signs, as Tim Ayling, VP EMEA at buguroo, explains: “With digital banking fraud on the rise, partly as a result of the pandemic, banks must ensure their customers are not being impersonated or manipulated at any point during their online sessions. Using AI and deep learning to conduct behavioural biometric analysis, banks can build unique ‘BionicIDs’ for all users, including cybercriminals. With the AI scanning for the smallest anomalies in the user’s behaviour, banks can perform continuous authentication, which can reveal malicious actors before fraud can take place, without impacting the customer experience.”
It’s important to remain realistic about where AI is best implemented, however. “While we don’t recommend AI be directly part of your core banking platform, it is very important for your core to be flexible enough to integrate with a partner that provides AI capabilities,” says Elliott Limb, Chief Customer Officer of Mambu. “Larger banks and financial institutions that haven’t implemented a flexible core banking platform can sometimes have trouble with digital interactions between their clients. Oftentimes they will struggle to be nimble enough to build something ‘on the fly’ that would be individualised enough for clients. For those larger institutions, AI will play a major role in the way they shape customer journeys and the way customers interface with banks.”
Catering to this demand for AI in the banking industry are a number of startups - many of which have attracted significant financial backing.
Cambridge, UK-based Darktrace has raised across eight funding rounds since its 2013 foundation. The company’s offering takes the immune system as its inspiration, with its main product, the Enterprise Immune System, bringing AI to cybersecurity and allowing for rapid, autonomous responses to incoming threats.
Tailoring its software to a number of industry verticals, of its financial services offering it : “Darktrace is uniquely positioned to defend against the full range of cyber threats. The self-learning AI technology detects and responds to all emerging malicious activity, reacting in seconds to protect organisations from zero-day exploits, insider threats, and machine-speed ransomware.”
Fraud detection is one of the most important responsibilities for financial institutions, and it’s here that AI is once again easing burdens. San Francisco’s Sift has to date raised for its platform, which utilises machine learning to protect businesses from everything from payment fraud to account takeovers. It does so by dynamically creating a trust score based on user behaviour.
“Companies have always had to choose between protecting and growing their business. With Sift Science, they no longer need to make this trade-off – they can reduce risk while also improving customer experiences,” said Sift Science CEO and Co-founder Jason Tan. “Our Digital Trust Platform already protects world-leading digital brands from fraud.”
Zest AI turns the power of AI onto the problem of credit underwriting, using machine learning to transform the business of accessing credit. The Los Angeles, California-based Zest AI has raised since its foundation, with its latest round last October seeing the company receive $15mn.
Its software allows banks to identify borrowers who are overlooked by traditional techniques, allowing lenders to tackle unfair biases in their approaches. Mike de Vere, CEO of Zest AI, : "Our customers want to spread economic opportunity more widely, but they've lacked the tools to make it easy to do the right thing. [...]We have the resources and commitment to bring the power of ML and new standards in fairness to every financial institution in the world."
The financial industry is certainly far from alone when it comes to embracing the disruptive power of AI. Thanks to the scale of the industry and the volume of money flowing through, however, it is one place where the beneficial effects of AI can be most keenly felt - whether in customer experience, digital banking or risk detection.