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Research has proven that banks are taking advantage of machine learning at the highest rate of all industries. What new doors does this open? Bill Lumley reports.

A volley of support from across the banking industry has been provoked by two separate reports this month that substantiate the implementation of machine learning (ML) across financial services. 

Banks and other financial services companies are nearly 20% faster in the adoption of ML than other sectors, according to a report by US consultancy Splunk on the economic impact of data innovation. This coincides with a survey published by the Bank of England suggesting ML is increasingly critical to the success of financial services operations.

The Splunk report finds 79% of financial services companies have adopted artificial intelligence (AI) or ML for data innovation, versus 67% overall. It emphasises the importance of investment to drive innovation. 

Commenting on the report, Mark Woods, Splunk chief technical advisor, Europe, the Middle East and Africa, notes that in financial services there are pockets of excellence in small teams, and there has been a significant change in this sector as these teams appoint senior leaders with data innovation in mind.

Meanwhile the Bank of England survey finds the number of UK financial services firms that use ML continues to grow. Overall, 72% of firms that responded to the survey reported using or developing ML applications. These applications are becoming increasingly widespread across more business areas.

Solving the data problem

“ML is all about the data,” says IBM’s managing director for financial services digital transformation, Prakash Pattni. “The more data of the right quality that can be fed to the model, the better the ML output is. Many companies struggle to identify all the required data sources, manage data quality and data security across environments.” 

He advises banks that, before adopting ML, they should ensure they have solved the data problem and have considered solutions such as ‘data fabric’ architectures that leverage hybrid cloud solutions, enabling users to access and surface accurate data wherever it is needed. Data fabric refers to a ‘layer’ of data services that ties a network together, rather than the traditional point-to-point approach.

New opportunities

Banks can enhance customer experience and generate significant cost savings through the use of ML, according to Richard Berkley, machine learning and financial services spokesman at PA Consulting. Until recently, banks had just been experimenting with ML and using it to gain better customer insights. Now, they are starting to build it into their core functions, he says.

Automated ML is now being embedded into credit decisioning processes, transaction monitoring for fraud detection, complaint resolution, identifying vulnerable customers, personalised customer service, recruitment and staff development, says Mr Berkley.

ML also has the power to uncover financial opportunities that may otherwise have been missed, according to Rajasekar Sukumar, vice-president, Europe at technology services company Persistent Systems. He suggests AI is playing a significant role in levelling the playing field between experienced investors and those with little experience.

it’s making product-targeting relevant to customers, increasing satisfaction and loyalty

Ove Kreison

“It does this by generating recommendations for the strongest portfolios in real time based on data and the individual’s risk profile, allowing people with no investment experience to get on the ladder of wealth creation,” says Mr Sukumar.

Ove Kreison, co-founder and CTO of Estonian core banking fintech Tuum, says most ML in banks and financial institutions is used for anti-money laundering and fraud detection, or in bigger banks as part of credit decisioning. But he stresses it could and should be used for much more.

“Machine learning should play a bigger role in banks making personalised product offerings. If you have a person travelling weekly – detectable by card payments in airports and train stations – their bank should be automatically offering this customer a credit card with travel insurance,” he says. “There’s a good chance they’ll buy it – directly impacting revenues – but regardless, it’s making product-targeting relevant to customers, increasing satisfaction and loyalty.”

Industry adoption

A Temenos-supported Economist Intelligence Unit survey on AI in financial services found that 85% of banking executives have a “clear strategy” for adopting AI to develop new products and services.

More than a third of those surveyed are prioritising AI to improve customer experience through personalisation, supported by acquisition of or partnering with fintech companies to enhance their customer experience in the offering of investments, saving deposits and retail lending. 

Hani Hagras, chief science officer at Temenos, says: “With AI, banking providers can create highly tailored services that address anticipated customer needs.” 

One of the reasons ML has not yet taken off at the trading level is because all the information banks need is stored across multiple systems and in different formats, according to Daniel Carpenter, CEO of Cognizant’s Meritsoft. “Linking this information together to predict things such as trade fail rates is nigh on impossible without digitising and normalising the data, and automating to ensure that data persists through the settlement process,” he says.

“Only once this has happened can banks even begin thinking about predicting where the future costs could lie using ML,” he adds.

The industry-wide research shows that banks are reassuringly ahead of other industries in ML, leaving them well placed to deal with the challenges of customer expectations and regulations.


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