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How will AI change the face of banking?

AML, Business Intelligence, Digital Attacks, Fraud Management, IDC, PwC, Retail Banking, Technology

February 04, 2019

  • AML
  • Business Intelligence
  • Digital Attacks
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Research firm IDC is predicting banks worldwide will spend more than $4bn on Artificial Intelligence (AI) in 2018. If we factor in PwC’s Sizing the Prize report to understand the broader trend for global business, it seems AI could add a further $15.7 trillion to the global economy by 2030. Undoubtedly AI will lead to a significant change in the way banking operates, but let’s consider what that might look like.

By Dr Giles Nelson, CTO Financial Services, MarkLogic

Firstly, think about customer relationship management in retail banking. Most traditional banks still have a largely transactional relationship with their customers, providing deposit and payment services. But consider for a moment the information that a bank typically holds about an individual – their financial history gives a unique insight into a customer’s commitments, preferences and desires. By using AI techniques to analyse this treasure-trove of information, banks can deliver suggestions to tailored financial products and, indeed, other consumer products.

This kind of personalisation has begun with the introduction of chatbots in banking. AI is enhancing customer relationships by using natural language as a way in which customers can interact and ask questions and promises better customer satisfaction and lower call centre costs.

Fraud and anti-money laundering (AML) are also perennial issues. AI techniques, particularly using machine learning, are used today in these areas, and their use will only increase as models and databases of source information get more sophisticated. This is also a constantly evolving area as anti-fraud staff battle with bad actors who are also employing the latest technologies. With richer datasets and more advanced AI techniques, this will only get better, leading to less financial loss and less annoying false positives. With the right data of past and current transactions, the typical behaviour of customers can be learnt, and anomalies detected. Transactions can then be stopped, perhaps even before they have occurred, or confirmation from the customer requested before the transaction can proceed.

Last but not least is AI’s impact on trading technology. Much investment over the last 10-15 years has gone into making automated trading systems, whether trading equities, FX or derivatives, faster and more responsive to changes in the market. AI techniques, such as neural network machine learning systems, have also been used for some time. As AI tools and the data available to them become more sophisticated and richer, so these systems will get better.  Better at spotting opportunities to trade, and better at spotting the occasional examples of abusive behaviour.

Tackling a fractured data landscape

This is all seemingly beneficial and promises a lot, but here’s the rub. AI thrives on lots of data. To make AI useful, data from different parts of an organisation need to be accessible so the AI systems can use it. Data sitting in remote technology silos may be vital to a particular application, but if it isn’t easily accessible it may as well not exist. What’s more, that data has got to be well organised too – there is no area of technology where the aphorism ‘garbage in garbage out’ can be applied more strongly than with AI.

Furthermore, the data systems underpinning AI need to be agile enough to deal with new challenges quickly. Businesses cannot afford to spend months waiting for the right data to become available before launching new services – by then the competition will likely have an edge.

So, having the right data technology foundations is critical to delivering the process of AI, but a lot of banking organisations today don’t have this and are dealing today with a fractured data landscape.

The path to data-driven decision-making

Data silos are an undoubted issue together with the rigidity of most conventional data management systems. If financial organisations can go beyond this – delivering a holistic view of their data together with the agile data models that can evolve easily as business requirements change – then they can become truly data-driven. Competitive advantage will come from how smartly that data can be accessed and deployed.

More personalisation of retail services will occur, and banks will have the opportunity to strengthen their customer relationships and become more valued partners with end customers rather than just providing commoditised banking services. This will enable banks to provide services traditionally only targeted at the wealthy through private banking, to a much bigger segment of their customer base. Similarly, risk, fraud and money laundering should all ultimately reduced with the greater insights that AI can bring, making the whole financial system safer.

As with any new generation of technology, change can be both positive and negative, but one of the most scrutinised areas of disruption is the jobs market. With the introduction of AI, jobs will also change, and that’s the key point. One of the main purposes of any new technology is to make people more productive and to get ‘up the value stack’. This will need a willingness on behalf of people to embrace and, indeed shape, new AI-powered tools.

AI has the power to transform the banking sector, but only with the right data infrastructure. Banks should be acting now to ensure they have the right tools in place to make the most of the data at their disposal.

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