Home Slider Bringing Banking and Finance into an AI-Driven Future

Bringing Banking and Finance into an AI-Driven Future

by internationalbanker

By John Gikopoulos, Global Head of AI and Automation, Infosys Consulting




The financial-services (FS) sector has taken a lot of lessons from tech companies in the last 20 years. The boom in tech-focused FS companies has amplified this and brought the technological revolution of banking and finance into our everyday lives. The great rise of fintech has changed the way we manage our money for good.

We’ve been introduced to a whole range of new financial services—from money-transfer apps to mobile payments, shares trading and investments to crowdfunding and peer-to-peer lending. These new services are perfectly suited to a generation of digital natives who expect instant access to digital services—without sacrificing simplicity, security and great user experience.

In the last few years, there has been an exponential increase in the structured data that is collected and used. And the inclusion of unstructured data-sets along with more traditional structured data has been increasingly important in driving both strategic and operational business decisions. This is one of the areas in which artificial intelligence (AI) and its affiliated technologies, such as machine learning and advanced analytics, will play a significant 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. And now, even the most traditional banking institutions are investing in these technologies to solve business challenges. Some of the most popular applications include compliance (sanctions and fraud screening), operational efficiency, customer experience, eliminating risk, needs analysis and product matching.

Eradicating risk

We all make mistakes, but few have to face the same consequences as the poor Samsung Securities’ employee who earlier this year wiped away 12 percent of the company’s stock price with a simple mistype. Collectively, 2,000 employees were due to receive a dividend of two billion, or just under a dollar for each share they owned. But the administrator accidentally issued two billion shares.

Businesses in the financial-services industry are uniquely vulnerable to these “fat finger errors”, and not just because of the volumes of high-value transactions that they make every day. The answer is to explore how robotic process automation (RPA) can be employed to eliminate human error (or, indeed, deliberate fraud). There is a lot of fear around automation “stealing” humans’ jobs—but for repetitive, rules-based processes involving high-stakes transactions, it’s more likely to safeguard jobs by preventing cataclysmic mistakes that can cost a company hundreds of millions of dollars at the stroke of a key.

Automation isn’t just about protecting against errors, important as that is. There is enormous potential to bring RPA into other areas of finance; for example, in areas such as compliance efforts, anti-money-laundering (AML) activities and know-your-customer (KYC) initiatives. What’s more, RPA can bring huge cost- and time-savings by automating many of the tedious, process-heavy transactions, such as account opening or customer service. And AI can bring even more benefits.

Fight back against fraud

In January of last year, Lloyds Banking Group was hit by an unprecedented 48-hour online attack from cybercriminals who sought to block access to around 20 million UK bank accounts. This attack served merely as a taste of what would become 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 layered security systems and regulators trying to mitigate risks, financial institutions are turning to technology to stiffen their defences. The most promising defence? AI.

AI is being deployed in a wide variety of roles, from customer authentication to suspicious-transactions examination. 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 experience.

While the use of advanced-analytics capabilities is not new to fraud management, AI and machine learning are 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.

Operational efficiency

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

Banks have embraced RPA for automating manual tasks and improving operational efficiency, but 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 the use of AI technologies in the review and even drafting of contractual documents.

For example, JPMorgan Chase has invested in AI technology to sift through mountains of legal documents and extract the most important clauses and data. The bank’s 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. JPMorgan Chase also 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 will be to effectively and efficiently address most of the 1.7 million employee requests that the company receives each year.

JPMorgan is an example of a financial-services firm putting technology firmly at the core of its business, across every function; recently, the company announced mandatory coding training for all of its asset-management staff, having so far trained around a third of its analysts. Training and developing these core technical skills is a major investment in the future of the company, enabling employees to lead major technological change across an organisation in a way that makes sense for the business. Using technology to re-imagine services, customer experience and strategy is what makes a business stand out and continue to thrive. Rather than keeping information-technology (IT) and business functions siloed, training asset managers to code brings technology together with strategy in a way that sets JPMorgan apart from the crowd. Their investment in AI only strengthens this market-leading position further.

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 is 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, AI software can make informed market decisions and can also react to split-second opportunities in the market at speeds that human stockbrokers simply can’t hope to match.

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.

Digital natives and the new world of customer experience

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 branch networks 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. In turn, this enables staff to undertake more value-added activities.

Why innovate?

It’s easy to understand why traditional banks have, until recently, been worried about the onslaught of new challenges brought about by digital-savvy start-ups and tech vendors treading on their patch. Now, banks have seen the value—both for customers and to their businesses—in creatively learning from, or even collaborating with, fintech companies in order to improve their own organisations. But the possibilities that AI holds are much more than simply innovation for innovation’s sake. AI will enable these institutions to reinvent themselves entirely—to improve their safety and compliance, and to stay relevant for consumers now and in the future.


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