Bank Industry Is Turning On To AI Technology
The pace at which companies are investing in artificial intelligence (AI) continues to gain momentum and the financial sector is not immune to this trend.
According to research by global management consultancy Accenture, banks that invest in AI and human-machine collaboration tools could boost their revenue by over a third (34 per cent) by 2022.
AI is considered one of the most important disruptive technologies for today’s banks, with a recent PwC survey revealing that 72 per cent of senior management see AI and machine learning (ML) as key sources of competitive advantage.
The survey also highlighted that 52 per cent of companies in the financial services sector are already making substantial commitments to AI, with 66 per cent projecting significant investments by 2020.
The finance sector has been using AI in very specific areas for some time, but we’re now seeing a rapid growth in take-up due to increasing market competition, the need to reduce overheads and the benefits of harnessing increasing volumes of data.
“Banks are experiencing massive competitive pressure from multiple organizations that don’t carry the same technical debt in the form of legacy software,” highlights Daniel Kroening, Professor of Computer Science at the University of Oxford, and CEO of Diffblue.
“Emergent fintech challengers, such as Revolut and Monzo, are fast to market, better at engaging customers via digital channels, while delivering user-friendly, more straightforward applications.”
As James Duez, co-founder and executive chairman of AI company Rainbird points out, the banking sector is also under pressure to meet the needs of a millennial audience.
“The under-35s are presenting new challenges for banks. This digital generation expects seamless, flexible, multi-channel platforms. They want fast responses and effective solutions to their problems, right now.”
There are a number of ways that the financial industry is using AI from algorithmic trading through to robotic process automation. Here we take a look at some of the latest trends in the sector.
Robotic Process Automation
Robotic process automation (RPA) that uses cognitive AI is being deployed by banks to improve operational efficiency and reduce costs, and many large finance firms are already seeing benefits.
For example, Bank of NY Mellon Corp. reported an 88 per cent upgrade on processing time and a 66 per cent improvement in trade entry turnaround time since implementing RPA.
Then there’s JP Morgan Chase, which devised the contract intelligence platform COIN. The bank claims COIN saved more than 360,000 hours of labor using bots capable of analysing contracts with unprecedented efficiency.
Therefore it’s not surprising that, according to data by analyst firm Everest Group, banks and financial firms account for 40 per cent of the RPA independent software vendor market.
Fraud Prevention and Detection
According to analyst firm Frost & Sullivan, automated proactive fraud detection solutions have been experiencing high adoption across the entire banking and financial services sector for the past five years.
Understandably the technology has become accepted as integral to a bank’s defences, and organisations such as Morgan Stanley and HSBC have even set up AI fraud detection ‘teams’. Well suited to such tasks, AI can spot anomalies and patterns in transactions better than the human eye.
“Applications of AI to fraud have been extremely effective,” says Imam Hoque, COO at big data start up Quantexa. “Previously, criminals could reverse engineer systems to basically ensure that they were not caught. With AI-powered systems it is extremely difficult for the bad guys to understand how the engine works, which makes it much more likely that criminals will be caught.
“Anti-money laundering (AML) detection can be particularly complex in capital markets, it is difficult to create simplistic rule sets that can be applied to the trading of FX, equities or precious metals. The application of AI, networks and context though data has enabled those systems to become effective for the first time.”
From a customer-facing perspective, AI has also been able to spot a growing volume of false positives for ‘suspicious’ card use.
Operational Intelligence
Not all examples of AI are as exciting as fraud prevention, but nonetheless they still have a profound impact on a bank’s ability to increase profits and reduce risk, particularly important when cybersecurity and regulatory compliance are high on financial firms’ lists.
Operational intelligence platforms combine sophisticated threat detection, tight controls on inside protection and AI’s ability to discover the unknown. Also, service stability is crucial to banks, as customers, regulators and shareholders are unforgiving in outage situations. People expect to have 24-hour access to their money.
Hannah Preston, Global Account Director at technology firm CA Technologies, expects we’ll see more of this in the sector going forward.
“Many banks have not changed their approach to dealing with outages and stability issues for 20 years. In fact, most still have big command rooms with monitors everywhere and people continuously watching these screens to spot deviations. They inevitably have high volumes of false alerts. And often Twitter reports the problem before the bank,” she notes.
“AI has the ability to transform this process entirely: not only by detecting stability issues, but by predicting them and applying automatic self-healing remediation.
“An individual large bank will spend hundreds of millions each year keeping things running as smoothly as they can in the old-fashioned way, but investing in AI can empower them to seamlessly avoid service disruptions and accelerate the clean-up of the problem.”
Chatbots / Virtual Assistants
One of the most popular forms of AI used by banks are chatbots, or virtual assistants, which can be largely explained by customers now being used to dealing with tools such as Siri and Ok Google.
For example, Swedbank’s Chatbot Nina deals with approximately 30,000 conversations every month and had a first-contact resolution rate of 78 per cent in the first three months of its deployment.
Plus Bank of America recently launched its virtual assistant Erica, which it says uses predictive analytics and cognitive messaging to provide financial guidance to its customers.
Customer profiling, recommendations and intelligent advisors
AI’s ability to detect subtle patterns in data enables it to provide detailed customer profiling. This has led to a growth in robotic advice services for everything from pensions and general wealth management through to mortgages.
For example, JP Morgan has a recommendation engine that tells clients what to do with their equity while Morgan Stanley is expanding the work of its 16,000 financial advisors as the result of the introduction of AI agents.
Credit Scoring
Credit scoring is another area where the banking industry is using AI. The technology extracts insights from data on contracts, past spending and future income to provide a range of credit decisions.
However, the use of AI for credit scoring does point to some risks, as Ralf Ohlhausen, Business Development Director at financial institution PPRO Group, points out.
“Unless the algorithms are rigorously tested and weighted to avoid replicating bias that already exists in the data, or introducing new biases by making false correlations, then there is the danger that credit rating AIs could unfairly deny some people access to financial services,” he notes.
Algorithmic Trading and Investments
In the financial sector, and hedge funds in particular, AI is also being used for algorithmic trading and the rise of data use in investment decisions, especially alternative data.
This technology is helping to leverage greater and more diverse sources of information, which are feeding into trading models. Financial organisations leading the way have included Black Rock, JPMorgan and Citibank.
Unlocking the potential of AI in banking
We’re right at the start of banking’s AI journey and although the sector’s making admirable progress, many tech firms believe it’s still gently testing the waters rather than confidently racing towards an AI-fuelled future.
Jane Jee, CEO of regtech company Kompli-Global, believes the highly regulatory nature of banking is one of the issues holding the sector back.
“They are cautious about adopting any technology which does not have the regulator’s support – even though the regulator cannot support individual solutions as it must not distort competition. I have lobbied for the regulator to experiment more with new technologies which support financial institutions as most of their sandbox candidates are firms which are regulated themselves,” she notes.
What the technology experts do agree on however, is that financial institutions must ensure they implement AI for the right reasons and not simply for the sake of it. However, recent research by AI provider Squirro has highlighted that there is a lack of understanding about how to draw on AI to improve business processes, with 83 per cent unaware of how best to use it to solve business challenges.
Gareth Hole, Solution Sales Manager, Advanced Process Automation at software firm NICE Systems explains that as with all tools, the best way of using AI is to target it at solving a specific problem and creating new opportunities.
“Rather than picking an AI capability and working out how to use it, you can often achieve more initially by having a vision of what you want to do, and then determining the most expedient way of getting there,” he explains.
“Once you realise how the technology solves an existing problem, it can unlock ideas about new uses for it. In this respect, the banking sector is generally taking the right approach of applying AI to specific business problems but there is a long way to go to truly unlock the potential of AI in banking,” he concludes.
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