AI in investment and financial services

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For years, the financial services industry has been trying to automate their processes, ranging from back-end compliance work to customer service. But the explosion of generative AI has opened up both new possibilities, and potential challenges, for financial services firms.

The whole world is in a discovery phase right now, says Saby Roy, a technology consulting partner at EY. We are seeing many organizations trying to put this into practice.

Applications of artificial intelligence in financial services

AI is already being used to try and improve the customer experience when it comes to financial services groups. Many consumers are familiar with the basic iterations of chatbots on banking and retailer websites, but these tend to be limited in functionality and rely on a set of canned responses.

Financial institutions now hope that generative AI can replace these systems with alternatives that are better able to respond to complex requests, learn to handle specific customer needs, and improve over time.

If you look at the customer service side, we’re seeing a lot of interest from customers in how they can put generative AI in place around chat channels, says Rav Hayer, managing director of management consultancy Alvarez & Marsals digital practice. There are many discussions about conversational finance.

Another area where automation has already taken hold is credit. Here, AI systems are used to review documentation and speed up the assessment of whether a consumer can afford credit products, such as mortgages.

We have 15 different AI models live on our platform, which perform different functions, explains Stuart Cheetham, managing director of mortgage lender MPowered Mortgages. Several models check which bank a bank statement comes from, examine its veracity, and turn it into machine-readable data that can be used to make a decision.

We do not allow any black box AI to be used near a decision making process

However, the system isn’t fully automated, Cheetham says, with humans still involved in making the final decision. Under the General Data Protection Regulation, consumers have some protections from fully automated decision making, in which no humans are involved.

We don’t allow any black box AI to be used near a decision-making process, he says, referring to systems whose processes cannot be clearly explained.

At the other end of the scale, AI is also finding applications in investing by helping fund managers transform raw data into something that can be used to make smart choices, whether stocks or other asset classes.

It gives you a much more forward-looking view, says Hal Reynolds, co-chief investment officer at Los Angeles Capital. It allows you to understand information much more efficiently so that you are ready to make a good investment decision.

Among the datasets their systems study are executives’ calls with analysts, where they can scan for clarity of purpose, analyst responses, and whether companies’ results measure up to what their bosses say.

Companies are also adapting generative AI to help fight financial crime, with a wide range of use cases including the slow and expensive but vital field of anti-money laundering and knowing customer protocols.

Earnings from the use of AI

It’s about saving minutes that lead to hours, says Gumundur Kristjnsson, founder and chief executive officer of Icelandic fintech Lucinity, which uses artificial intelligence to support bank staff as it tries to detect money laundering and other wrongdoing.

Lucinity’s co-pilot system, Luci, transforms transaction and individual alerts into text, allowing agents to evaluate them faster and can write a case summary, accelerating agents’ ability to work on their caseload and handle more potential issues.

I’ve been in AI really for 15 years, the rate of innovation has gotten so fast, notes Kristjnsson. Tools are becoming more accessible so that a small business like ours can reap the benefits.

Bigger players are also using AI to fight fraud, an issue that cost the UK £1.2bn in 2022 according to industry trade body UK Finance, including Mastercard.

In early July, the payments processing group unveiled its new Consumer Fraud Risk system, which gives banks an individual score on the likelihood of a transaction on the UK’s Faster Payments network being fraudulent within milliseconds, based on previous work on Money Mule accounts used for money laundering.

The High Street Bank TSB, which has been testing the system since January, estimated it could reduce the instances of authorized push payment fraud in which users are tricked into sending money to criminals by around 20%.

Scale and delivery are what’s different with AI, says Ajay Bhalla, president of cyber and intelligence at Mastercard. Banks can stop term fraud before the money is transferred.

Risks arising from the use of AI

But experts are also concerned about the risks of artificial intelligence, including its ability to enable financial crime. Alvarez & Marsals Hayer points to concern that scammers will implement generative AI to make their attempts to steal data and money more effective, such as by better impersonating a senior colleague in an email.

Previous implementations of automated tools have also faced controversy over the impact of their failures, such as wrongful arrests in the US due to the limitations of facial recognition technology. For Hayer, this means that it is crucial that institutions consider the risks as much as the opportunities.

Governance will be absolutely key, he says. How do you reap and spread the benefits of AI without unleashing a host of unintended consequences or creating something that is ultimately destructive?