“Until very recently, Artificial Intelligence (AI) has been something talked about by science fiction writers and worked on in the depths of university IT research labs. With the explosion of interest in the last few years, all this has changed.”
Comments Meghna Asthana, an Artificial Intelligence professional with a Master’s degree focused in Biomedical Engineering (Neurotechnology) from Imperial College London.
AI – as a source of excitement, change, innovation and some may even say, fear – is on the verge of penetrating every major industry, be it healthcare, advertising, transportation, legal, or others. We have interviewed Meghna Asthana to listen to her insights on what impact AI technology can have on the financial services industry in the near future.
Hello Meghna! Thank you for agreeing to speak with us on the topic. So to start off, what do you think are the major ways Artifical Intelligence technology could promote greater efficiencies and address the biggest pain points in the finance sector?
The first thing off the top of my head would be fraud prevention. In order to protect clients’ data against increasingly external threats, we must consistently stay one step ahead of hackers. With the ability to compare each transaction against an account’s history, AI possesses the ability to pick up behavioural habits and out-think the human, thwarting security breaches. Machine learning algorithms are able to assess the likelihood of unusual activities (eg. out of state purchases, large cash withdrawals, etc.) deeming the transaction to be fraudulent.
The second would be investment predictions. In recent years, hedge funds have increasingly moved away from traditional predictive analysis methods and have adopted machine learning algorithms for predicting future trends. Using machine learning, fund managers hope to identify market changes earlier than is possible with traditional investment models.
With regard to customer service, the solution provided by machine learning technology is not to replace automated customer support systems, but to make them better. The tremendous power of machine learning technology to access data, recognize patterns, and interpret behaviour means that the technology can be used to create automated customer support systems that mimic a human agent, with the ability to understand and respond to uncommon concerns. By making phone and online customer support portals more human-like, financial institutions can provide efficient support that reduces customer blowback.
Another point to consider would be marketing. The ability to make predictions based on past behaviours is fundamental to any successful marketing effort. By analyzing web activity, mobile app usage and response to previous ad campaigns machine learning software can predict the effectiveness of a marketing strategy for a given customer.
What, in your opinion, is the biggest technical obstacle facing AI adoption in financial
services? What issues would be prevalent in the long term?
I believe a lack of compute power would definitely be one of the big ones. AI – specifically the machine learning and deep learning techniques which show the most promise, require a huge number of calculations to be made very quickly. This means they use a lot of processing power. Cloud compute and massively-parallel processing systems are what have provided the answer in the short term. But as data volumes continue to grow, and deep learning drives the automated creation of increasingly complex algorithms, the bottleneck will continue to slowly progress.
A lack of people power would be another one. Until very recently, AI has been something talked about by science fiction writers and worked on in the depths of university IT research labs. With the explosion of interest in the last few years, all this has changed. But there are still not enough people to enable every business or organization to unleash their vision of machine-powered progress on the world. Just as in other areas of science and technology there is a skills shortage – simply not enough people who know how to operate machines which think and learn for themselves.
Another important one would be one track minds. A vast majority of AI implementations in use today are highly specialized. Specialized AI, often referred to as “applied AI”, is created to carry out one specific task and learn to become better and better at it. It does this by simulating what would happen given every combination of input values, and measuring the results until the most effective output is achieved.
From a technology standpoint, what does it take to make artificial intelligence truly
The one true aspect that would make AI mainstream is an advancement in current technologies
like Natural Language Processing and Prediction Models. An increase in computing power
which could easily enable engineers to enable Machine Learning techniques would highly
improve AI’s status in providing the best services and finally, make it mainstream.
On the last note, there are concerns that technology will soon become so powerful it will replace people. What’s your take on this in regards to the financial services industry?
I personally believe that technology will not replace but will go hand in hand with their
human counterparts. AI would improve and augment their experience as professionals and
increase the overall productivity of the business.