Rajashekara V. Maiya
Head of Product Strategy, Infosys Finacle (Moderator)
A good way to begin any discussion on what banks should do with Artificial Intelligence is to look at what the four tech-biggies are up to. On the infrastructure side, there’s Amazon Web Services and data center, Google Cloud, Apple SIM, and now, a solar-powered Internet spreading drone called Aquila from Facebook. In the world of mobile communications and instant messaging, there’s Plus, Duo and Allo from Google, FaceTime from Apple and Facebook Messenger. Connected technologies are also inviting interest from Google (Google Auto), Amazon (Alexa driven-integration), and Apple (AirPlay/ Car). Besides this, the GAFA set have their own smart assistants namely, Google Assistant, Amazon Alexa, Facebook Jarvis and Apple Siri.
A recent report estimated that globally, the tech giants (GAFA, Baidu and others) spent between US$ 20 billion and US$ 30 billion on AI in 20165. All these companies are betting on an AI opportunity that is expected to contribute US$ 15.7 trillion to world GDP by 2030, of which US$ 6.6 trillion will result from productivity gains and the remaining from consumption effects6. 2017 marked the beginning of the 4th digital wave sweeping through 25 countries around the world, and driven by robotics and AI. As AI overlaps with the Internet of Things, there’s no telling how many devices and people it will connect together in the next few years, but one thing is certain, which is that the future will witness massive collaboration between human and machine intelligence.
Where do banks figure in all of this?
Traditionally a laggard in technology adoption, in AI the banking industry has a chance to right that record. This is because industries that have already gone digital in a big way – and financial services is one of them – are also best placed to adopt AI. But when we commissioned a survey of 1,600 business and IT leaders from 10 vertical groups to understand what their organizations were doing in this space, half the respondents said that not knowing where AI could help was one of the biggest barriers to adoption.
One way of finding out is to use a framework proposed by the McKinsey Global Institute to assess where AI fits into the value chain of any business. Does it help projection processes by improving R&D and forecasting?5 Does it optimize production and maintenance? In what ways does AI benefit sales and marketing? And above all, can the enterprise provide better user experiences using the technology?
Applied to banking, this framework says that AI could project things like new consumer demands, changes to the regulatory landscape, competitor activity and the extent of customer churn. More importantly, the data and analytics layer of AI would be able to analyze the root cause of various events and recommend the best response to each. Currently, most banks are sitting on 10 to 15 years of mostly idle data. AI would enable them to extract its insights to project future events and optimize their impact on the organization.
Moving further down the value chain, AI technologies, such as robotic process automation and machine learning, would eliminate the manual effort going into routine, repetitive banking processes to deliver enormous efficiency and productivity gains. For example, at JP Morgan Chase, a program called COIN interprets loan documents in seconds, with a high degree of accuracy. Previously, the bank employed a very large number of loan officers and lawyers who spent 360,000 hours every year on this task7.
Similarly, the framework can locate a number of use cases where AI can add value by identifying the right targets for product promotions, or by improving the delivery of services to provide superior customer experience.
Now it is up to each bank to decide which use cases to lead with, based on core competence, objectives, resources and readiness. (It is important to be aware that there is no single formula that will work for all banks). After identifying their top use cases and enabling AI technologies, banks should lay the groundwork by provisioning the required IT infrastructure, ensuring connectivity and mobility, and making the necessary investments without delay. AI is still evolving, which means that there is significant advantage to be had by early adopters. On the flip side, fence sitters and slow movers risk falling behind to a point of no comeback.