AI-ming high

Written by: Marco de Novellis Posted: 22/11/2021

BL75 AI illoAcross financial services and the wider business world, artificial intelligence and machine learning have the power to deliver a customer experience that is quicker, more flexible, more autonomous and more personalised – while bringing organisational benefits to boot. But hurdles remain…

You’re probably used to speaking to virtual chatbots by now. Maybe your bank card’s been blocked or you’ve proven your identity just by looking into your iPhone’s front-facing camera.

Artificial intelligence (AI) and machine learning (ML) are already well-established tools in the finance industry. According to the OECD, financial markets witnessed global spend of more than $50bn in AI across the finance and business sectors in 2020 alone.

For financial services providers, much of that spend is intended to improve the customer experience. In a recent Capgemini survey, nine in 10 leaders of major finance firms said boosting customer experience was the key objective behind launching new AI-enabled initiatives.

So what’s driving this race to invest technologies to drive the customer experience? For Martin Keelagher, CEO of Agile Automations, which provides process automation services to financial organisations, implementing AI and ML is about increasing efficiency – for both end-users and the businesses that deploy those techniques.

AI tools can easily automate repetitive tasks, digest and interpret vast amounts of data, and then learn from the data to improve processes. AI also allows banks to respond to many customer service inquiries 24/7, or to analyse thousands of data points simultaneously to spot a potential security threat. 

“These are things humans wouldn’t be able to do in anything like the time it takes an AI system,” Keelagher explains. By freeing up time through automation, he says, organisations can provide faster, better customer experiences – and efficiency for themselves in-house.

AI machines are also more reliable than humans, helping organisations reduce the risk of human error and increase control over their processes.

“A robotic workforce doesn’t get sick or take time off; it does the same thing day after day with the same level of competency. That’s especially useful during a pandemic, when organisations might have a significant variation in their workforce,” says Keelagher.

According to Capgemini, firms that implement AI in customer-facing functions also see a positive impact on their bottom line, including reduced cost of operations and increased revenue per customer.

In banking, McKinsey estimates that AI technologies can deliver up to $1trn of additional value each year.

Some of this can be realised in terms of full-time equivalent savings – the money you save by taking a human out and automating a process with a machine. 

But the return on investment that businesses can expect from AI goes beyond that. Barclays, for example, was fined £72m by the Financial Conduct Authority in 2015 for failing to run thorough checks on clients involved in a transaction worth £1.9bn.

AI tools that are better able to prevent illegal transactions reduce the risk of financial crime and help organisations avoid hefty fines.

It’s also only by investing in AI that providers can offer the digital, flexible, on-demand services that customers increasingly expect.

“We’re at a point now where you can pick up a phone and instantly access your bank or investment portfolio,” says Keelagher. “Organisations must invest in the proposition that allows that speed of access. When calculating ROI, you’ve got to ask: ‘What’s the cost of us not doing it?’”

Practical applications of AI/ML

The theory behind what these technologies can do and the benefits they can deliver has been well documented. But what do these technologies really look like? 

In fact, there are various practical applications of AI being implemented, carrying out a variety of roles, which includes the following:

• AI chatbots, or virtual assistants, answer commonly asked questions online, 24/7. When new questions are asked, the bot learns from them and improves the quality of its answers over time.
• Robo-advisers provide automated, algorithm-driven financial planning services, analyse investment portfolios and use data to offer advice to investors and make automatic investments.
• In banking and other financial sectors, running an AI algorithm over financial transactions provides insight into a customer’s banking habits, and AI tools can make suggestions to help customers manage their finances. AI tools can also help banks and credit lenders make smarter credit decisions.
• The most common application of AI, however, is in robotic process automation, with systems learning off the data and becoming increasingly smarter and more efficient.
• Machine learning, which recognises patterns in data, spots anomalies and corrects errors, has uses across governance, risk control and fraud prevention. After ML recognises potential fraud, it can automatically put a blocker on a client account.

BL75 AI illo2Aside from these practical roles, AI also has the potential to transform organisations’ Know Your Customer (KYC) capabilities, the key anti-money laundering measure carried out by finance firms to verify the identity of clients and investors.

Tim Andrews founded The ID Register to provide a centralised, regulated and digital hub for investor onboarding and KYC, so investors can complete a due diligence profile once and avoid dealing with repetitive KYC requests.

AI, he says, improves the KYC process for organisations by linking different information together from disparate sources to build a more comprehensive picture of the investor.

While The ID Register still uses human judgement to authenticate institutional investors, other consumer-facing firms, such as Monzo, use AI-enabled biometric authentication to onboard new customers, recognising government-issued IDs and matching them to customers using facial recognition technology.

Human plus machine

Despite advances in AI, most customers still value human interaction. Of 3,500 financial services consumers surveyed by Capgemini, 35% said their interactions with AI chatbots lack a human touch.

Only humans have the empathy required to effectively handle customer complaints. True AI, a system that’s sentient and actually creates human intelligence, simply does not exist. Financial services providers are therefore leveraging AI to augment their staff, not to replace them.

“I can’t think of a case where we’ve automated something that’s resulted in 20 people being made redundant. Instead, people are assigned to roles that are more fulfilling for them,” Keelagher says.

The AI workforce combined with the human workforce gives firms capacity, reliability and resilience combined with empathy and human interactions. AI fills out spreadsheets, while humans focus on client relations.

“This is still a high-touch industry and people don’t want that cold face of a machine,” says William Harris, Group Chief Digital Officer at investor services provider IQ-EQ. “We use AI to support our staff to become faster, more accurate and more efficient, so we can provide better financial advisory services done by humans.”

Adoption challenges

Even so, when you talk about AI, some people still envisage the Terminator on a killing spree or robots taking over our lives. Others are concerned about privacy, or wary of murky AI algorithms tracking and using our personal data. 

As a result, AI needs some good PR, says Harris, a self-proclaimed AI evangelist. “We need explainable AI; to understand what data points an algorithm is ingesting, how it’s interpreting those points, and how it’s reaching an outcome,” he says.

“To build trust, we need to talk about AI in a way that’s easy to understand, and show it’s a good thing that can improve people’s lives.”

Security plays a key role in building trust. Harris works with a team of cyber security experts that hosts ‘war games’, where programmers hack their own systems to test security. 

Again, however, the challenge is one of perception. While privacy is a valid concern, Keelagher says machine learning actually makes customer data more secure. “An intelligent system will recognise when its experiencing a cyber attack and do something about it.”

Similarly, the main reason investors move to The ID Register is security. Typically, KYC is completed on a spreadsheet and the process is balkanised across financial institutions, with each following their own policy. 

As Andrews says: “Investors want more control over their own information and who has access to it.”

Data challenge

For organisations, the most significant adoption challenge is data. You can’t have AI and machine learning without a strong data set to operate from, but acquiring, storing and making data accessible can take time and money. 

Larger organisations with complex legacy systems, such as high-street banks, will need to make substantial investments in IT infrastructure and pool data into one usable source in order to truly harness the potential of AI.

Despite all its significant setbacks, the pandemic has brought about a new era for AI. According to a survey of 47,000 banking customers conducted by Accenture in late 2020, 50% said they now interact with their bank via mobile apps or websites at least once a week, compared with 32% before Covid.

In addition, 54% want AI tools to help them monitor their budget and 41% are “very willing” to use computer-generated banking advice. “The pandemic has advanced the understanding and acceptance of AI by a decade,” says Harris. 

With humans out of the office and resources stretched, Covid has also forced organisations to invest more in technology.

In banking, the customer experience of the future will be quicker, more flexible, more autonomous, and more personalised, Harris predicts.

Consumers will apply for mortgages and credit cards on-demand. Predictive analytics will change the way we manage our personal finances, and the continued advance of open banking will mean we’ll manage all our accounts in one place with a single point of interaction.

Investors, too, will leverage AI to make ad hoc requests, Harris continues. “Rather than going to a wealth manager, you’ll come to a centralised client portal and get your current net asset value calculation in a matter of seconds.”

Ultimately, Keelagher believes, the implementation of AI is about making the human experience better. “AI is going to free up people’s time and improve work-life balance, changing how people interact with their jobs and bringing about higher rates of retention in the workforce.”

While getting organisations to adopt AI remains a challenge, at least now the door is open. Harris reflects on the success of business magnate Henry Ford.

“Consumers wanted faster horses and he provided them with the motorcar. This is our opportunity to do that in financial services. Instead of providing faster customer services, we can use AI to fundamentally change how our services are delivered.” 

At a glance: AI and ML

The difference between artificial intelligence and machine learning:
• AI is an overarching concept to create intelligent machines that can simulate human intelligence and behaviour.
• ML is a subset of AI, referring to systems that can learn from data by themselves.

AI/ML’s key benefits
• Freeing up time through automation
• Reducing cost of operations
• Increasing revenue
• Enabling remote services
• Preventing fraud.


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