Against a global backdrop of emerging technologies and ever-changing relationships between business and consumers, one thing is clear: leaders in the finance and banking industries will increasingly look to artificial intelligence to understand and engage with their customers in new ways.
According to a 2017 Forrester report, the widening gap between financial firms that embrace digital growth and business transformation powered by technology, and those institutions that continue to do business in traditional ways will continue to widen.
The intellectual roots of AI and the concept of intelligent machines were first found in Greek mythology. Intelligent artifacts appear in literature, and since then, mechanical devices that have been created have demonstrated similar behavior to some degree of intelligence.
The term is frequently applied to the project of developing systems endowed with the intellectual processes characteristic of humans, such as the ability to reason, discover meaning, generalize, or learn from past experience. Mathematical analysis of machine learning algorithms and their performance is a well-defined branch of theoretical computer science often referred to as computational learning theory. It relies on deep learning to accomplish tasks by processing large amounts of data and recognizing patterns in the data.
Deutsche Bank chief executive John Cryan once mooted replacing as many as half his 98,000 staff with robots. His dramatic ousting means he will not be held to that promise, but investors need not look far for other bankers talking up the potential of artificial intelligence to revolutionize an industry that has struggled with profitability in the decade since the financial crisis.
Santander introduced red robots to show guests around their Spanish visitor centre in 2010. UBS has Amazon’s digital assistant Alexa on customer service duty, JPMorgan is using robots (the invisible kind) to execute trades and Morgan Stanley has an AI fraud detection team. Just this week, HSBC said it would follow suit by using AI to detect money laundering, fraud and terrorist funding.
In India, private-sector banks are looking at using innovative technology to improve worker productivity,; public-sector banks like State Bank of India (SBI) and Bank of Baroda have started deploying AI to increase overall efficiency and customer experience while reducing operational costs.
To fuel its AI mission, SBI launched a national hackathon, “Code For Bank”, for developers, startups and students to come up with innovative ideas and solutions for the banking sector, focusing on technologies such as predictive analytics, fintech / block chain, digital payments, IoT, AI, machine learning, BOTS and robotic process automation.
Banks are using the following to improve efficiency and customer experience: chat bots and voice bots to interact with customers and solve problems before any human staff get involved; offer personalized communications and decisions based on detailed profiles of each customer; spot the anomalies or patterns of transactions which might indicate fraud and money - laundering and finally some decision-making could be made by AI’s operating with complete knowledge of the regulations and laws in each territory.
Financial modeling based on historical experience can throw out key metrics that will need higher level of focus and may not be amenable for significant standardization.
“The problems we have solved are very narrow,” says Foteini Agrafioti, head of Royal Bank of Canada`s research arm Borealis. “The misconception is that humans and machines can perform at the same level. There’s still a long way to go and many challenges we need to solve before a machine can operate [at a level] even near the human mind.”
Professor Patrick Henry Winston, who headed MIT’s AI lab between 1972 and 1997 and is now a Ford professor at the institute, shares Ms. Agrafioti’s concerns about its limitations.
Our assessment is that it may not be prudent to have a standardized model of lending or collections, but rather evaluate business opportunities based on the demands of the situation. We feel that the efficacy of any advanced technology system depends on how uniform we are able to make the process so that the system can take automated decisions, in order to improve turn around time and also reduce the errors due to manual intervention. We believe that debate on standardization versus customization makes technology implementation challenging as well as interesting. Every business needs to evaluate the aspects of its operations that can be standardized without compromising the asset quality - and robust financial modeling will help in this. Financial modeling based on historical experience can throw out key metrics that will need higher levels of focus and may not be amenable for significant standardization.