Navigating disruption in the financial industry with Artificial Intelligence

Navigating disruption in the financial industry with Artificial Intelligence

Navigating disruption in the financial industry with Artificial Intelligence

A working definition of Artificial Intelligence (AI) is the “theory and development of computer systems able to perform tasks that normally require human intelligence.” While this explanation is useful, it doesn’t capture the enormity of the AI phenomenon, being billed as a massive change agent in the financial services industry.

The interesting bit is that the scope and impact of AI in the industry is not limited to technology alone.

The way we interact, decide and learn is increasingly being influenced by new technologies, AI in particular. More and more enterprises are waking up to the potential of AI, as was evident in a recent survey commissioned by Infosys, where 64% respondents stated that their organisation’s future growth is dependent on large-scale AI adoption.

While financial services is in the early stages of AI adoption, it is, according to the above survey, already the biggest investor in this technology. According to our survey, 56% of financial services respondents said they were using AI in the past 1-3 years.

The study found that financial organisations invested much more in AI than other businesses ($14.6 million versus an average $6.7 million for all respondents).

Banks have progressed from using robots to greet branch customers, to deploying AI for managing risk, combating fraud, devising investment portfolios, placing trades, and more.

Infosys has developed a programme to facilitate banking compliance, which compresses a huge and complex set of policies into an easy-to-navigate-and-update graphical representation. Sophisticated use cases like this one are unfolding every day.

AI is proving to be a panacea for larger banks and financial services institutions that are facing increasing pressure from more agile FinTech startups. In some cases, the benefits of AI are clear — for instance, facial recognition technology is 10 to 15 times more accurate at identifying people when compared to human beings.

AI software is also proving to be an efficiency nugget in contact centers, where it is responding to customer queries faster and more accurately than service agents. Far from depriving customer interactions of the ‘human touch’, AI is enhancing them with accuracy and insightful intimacy.

Then there are some instances where AI is solving inherent, even cultural challenges for banks. Take for instance, the case of Japanese banks Mizuho Bank and Mitsubishi UFJ, who are amongst the early adopters in using AI driven robots for customer service.

While Mizuho bank’s robot, called Pepper, entertains customers with games and multimedia, also providing product information on request, Mitsubishi’s Nao greets customers at branches and enquires about the services they might need.

This is a great way to counter the country’s shrinking workforce and create greater bandwidth for the banks’ taskforce to focus on more than just the routine of customer service.

Beyond the front office, banks are also experimenting with AI applications at the back-end.

In credit risk management, banks are leveraging smarter algorithms produced by machine learning and prescriptive analytics to understand repayment patterns, identify debtors, predict default and prevent fraud.

One of the biggest banks in the US, JPMorgan Chase & Co, is using a machine learning programme called COIN (Contract Intelligence) to review commercial loan agreements in seconds, a task that would take a team of lawyers and loan officers 3,60,000 hours each year.

At PayPal, an AI engine built on open-source can not only spot suspicious transactions, it can also tell a false positive from a real case of fraud.

Banks are also increasingly using AI for governance and compliance applications. Most of the compliance processes are well established, and thus, ripe for automation.

These include trade mismatches, compliance reports, regulatory information such as CCAR stress tests, client reporting and data remediation initiatives. Bringing in AI technologies for these tasks is helping FS firms to create a measurable audit trail of activity, reduce human error and improve overall regulatory reporting.

Apart from these quantifiable benefits, the biggest contribution of AI-led technologies, in my opinion is their ability to amplify, rather than replace, human intelligence.

AI offers banks a chance to redeploy displaced workers, now free of repetitive tasks, into more exciting, purposeful roles that call for skills like problem finding, creative thinking and innovation that will ultimately result in better financial products and services, new credit scoring models, and superior experiences.

AI will also create a need for new, high-end skills in data science, robotics and natural language processing, amongst others.

Financial institutions should therefore embark on a strategy of purposeful AI to elevate productivity and efficiency, but also go beyond that, to achieve an ambitious agenda of identifying and articulating new problems and ameliorating them with unprecedented solutions.

In the course of conversations and current engagements with banking clients, we have been seeding ideas for breakthrough AI-led innovation, which have been received with a great deal of interest.

So, for any progressive financial service institution, not only is there no going back on AI, there’s a very clear imperative to go fast-forward. As screen-savvy millennials enter the banking ecosystem, the possibilities of technology-led, yet human-like interface, are going to be immense.

(The author is President, Infosys, and Head — BFSI, Infosys)