Putting the (artificial) intelligence back into banking

By Jayakumar Venkataraman | 2 March 2018

Financial services and technology vendors make for uneasy bedfellows. While tech has formed banking’s bedrock since the Big Bang deregulation of the 1980s, in the last decade financial services (FS) organisations have seen the new “masters of the universe” steadily – almost stealthily – encroach on their patch.

Established tech vendors and new start-ups have introduced a range of financial services from money transfer apps to mobile payments, crowdfunding to share trading and investments. These new services are perfectly suited to a generation who have grown up with smartphones and expect instant access to digital services, combined simplicity and a great user experience.

While over the last few years there has been an exponential increase in the structured data that is collected and used, the inclusion of unstructured data sets, pictures, images and videos along with structured data has been increasingly important in driving both strategic and operational business decisions. This is one of the areas where newer technologies such as Machine Learning and Artificial Intelligence (AI) will play a significantly differentiating role in the way they are applied.

There are countless fintech start-ups that are leveraging these disruptive technologies by applying them to a number of innovative use cases. Some of the popular use cases are in the areas of compliance (sanctions and fraud screening), operational efficiency, customer experience, needs analysis and product matching.

Fighting fraud

In January last year, Lloyds Banking Group was hit by an unprecedented 48-hour online attack from cybercriminals who sought to block access to around 20m UK bank accounts. This attack served merely as an amuse-gueule for a sustained campaign of cybercrime from a bewildering array of increasingly-sophisticated hackers, ranging from organised criminal gangs to nation states.

Under pressure both from attackers trying to breach the layered security systems, and regulators trying to mitigate risk, financial institutions are turning to technology to stiffen their defences – AI in particular.

AI is being deployed in a wide variety of roles, from customer authentication to examining suspicious transactions. For example advanced analytics and machine learning technologies can give a fraud “score” to transactions within milliseconds, highlighting fraudulent purchases or approving real ones without any human intervention – or any impact on the customer’s experience.

While the use of advanced analytics capabilities is not new to fraud management, AI and machine learning is taking banks’ defences to an entirely new level. Able to consider hundreds and even thousands of parameters when looking for suspicious patterns of activity, machine learning is proving faster, sharper and more accurate at sniffing out fraud.

More efficient operations

Financial services is one of the operationally intensive industries, involving a number of manual and automatable tasks for even simple things such as account opening or payments processing or loan approvals.

Banks have embraced Robotic Process Automation (RPA) in automating simple manual tasks and improving operational efficiency. However the addition of cognitive capabilities using Machine Learning and AI will significantly improve the robustness of the automation initiatives and drive sustainable improvement in the efficiency of overall operations.

Some of these use cases include use of AI technologies in the review and even drafting of contractual documents. For example, JP Morgan Chase has invested in AI technology to sift through mountains of legal documents and extract the most important clauses and data. The bank staff typically spend an estimated 360,000 hours each year reviewing 12,000 commercial credit documents – a task that can now be accomplished in a much shorter time. The firm plans to officially roll out its virtual assistant technology which integrates a natural language interface to respond to employee technology service desk requests. The key objective of this will be to effectively and efficiently address most of the 1.7 million employee requests the company receives each year

Tools of the trade

Electronic trading has been around for decades, and computer algorithms have a long and distinguished pedigree in the financial services sector.

One important instance of this in high-frequency trading (HFT), a subset of algorithmic trading that is focused on volume, speed, and autonomous decision-making. By using the data that is funnelled into the system, the AI software can make informed market decisions and can also react to split-second opportunities in the market in ways that human stockbrokers can’t - human brokers simply cannot move quickly enough to make such trades.

Speed isn’t the only factor driving the adoption of AI-based trading. Some algorithms are beginning to learn how to trade on their own through a variety of machine-learning methods. Whether it’s through Bayesian networks, evolutionary computation, or deep learning, corporates and start-ups are leveraging the access they have to massive amounts of data, in order to train machines to automatically recognise and predict changes in the market.

Customer experience for the Millennial generation

Traditional banks were slow to see the threat from technology vendors and fintech start-ups, who used the concepts of simplicity and user experience (UX) to transform the way that people manage their money.

Most banks today still rely on call centres and their branch network for the vast majority of customer requirements, but AI promises to sweep away these costly and – for customers –inconvenient ways of working.

AI is the new frontier for customer service and banks have already invested in intelligent Chatbots and AI based customer service agents to provide 24/7 customer engagement and enable staff to undertake more value added activities.

Until recently traditional banks were rightly fearful of the challenge from digital native upstarts and established technology vendors muscling in on their patch. The relationship between the start-ups that leverage the disruptive technologies in a few smart use-cases and banks that require such solutions across many use-cases and at scale has not been easy.  Banks are looking at creative ways to collaborate with the fintechs to augment and strengthen their offerings. Technology such as artificial intelligence, however, shows how they can reinvent themselves to become safer, more compliant, and more relevant for many new generations to come.

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