Artificial Intelligence (AI) has all but become a dinner table conversation now, thanks not only to its exponential adoption but also to a very public disagreement on the topic between two individuals running two of the world’s most innovation-driven companies – Mark Zuckerberg and Elon Musk.
Two factors fundamental to the world of banking and finance make it the perfect use case for an Artificial Intelligence (AI) boost – the quantitative nature of the industry, combined with millions of customer interactions taking place every second globally.
Cathy Bessant, the CTO of Bank of America, shared her thoughts about the applications of AI in the financial world in a recent interview with Forbes. She has echoed what a number of technology officers around the world have been saying for some time now: AI is transforming the industry as we speak!
Banking and Finance is a sector which touches all of us at some point or the other. Whether we are business owners, vendors or customers – we have been riding the digitization wave knowingly or unknowingly. And as time passes, this influence will only spread wider and deeper.
In this article we scratch underneath the surface of the futuristic field that is Artificial Intelligence, to understand what it means and what it does not, and to finally see how it is currently used to transform global banking systems.
There are a lot of technical terms that get tossed around rather loosely in formal and informal conversations. Terms like “automation,” “Artificial Intelligence (AI),” “Machine Learning,” “Deep Learning,” etc. are often used interchangeably. Consequently, before we take a look at AI’s applications in the financial sector, it seems essential that we understand what AI actually means and, more importantly, what it does not!
Put simply, Artificial Intelligence refers to the idea of training machines to think like humans. The term was born decades ago in the works of American computer scientist John McCarthy who coined the term in 1955 and organized the famous Dartmouth Summer Research Project on Artificial Intelligence a year later, possibly the even which made AI a field.
AI has been making headlines in recent years due to the emergence of Big Data – i.e., the enormous amount of data handled by organizations. Organizations gather vast quantities of data today and are seldom equipped to handle it in the most efficient manner. And this is where AI works wonders. It can recognize patterns, derive models, solve problems and make decisions more efficiently (and accurately) than humans.
The best way to understand AI is if we start with automation, as it has been part of our lives for many, many years without us even realizing.
Those frequent mailers you see when you subscribe to a website’s news feed, the never-ending SMS you get on your cell telling you about coupons and the latest offers by some of your favourite stores, the notifications from a number of apps and websites such as Facebook, the instructions which help you set up your bank accounts or the millions of lights which get switched on at dusk and off at dawn – all of these tasks follow an “If X Then Y” pattern.
Think of automation as the ideal employee – a sincere worker who never gets tired, never asks for a holiday and keeps repeating the chores you instruct it to do with the same efficiency and effectiveness as the first time. The operative word here being “instruct.” Automation allows you to eliminate manual intervention from monotonous, repetitive tasks that need to be performed hundreds, thousands or even millions of times.
Artificial Intelligence, on the other hand, is markedly different. We have all had our taste of science fiction movies that depict the inevitable rise of machines capable of thinking for themselves and eventually wiping out humanity from this universe.
In fact, machines “thinking for themselves” should be the pinnacle of AI. In a contemporary context, computer programs that can find patterns in vast amounts of data and make decisions for themselves is AI. Unlike automation, AI-powered programs and machines get better at decision making with time.
The ultimate goal of AI is un-programmed, non-supervised automation.
The scope of AI is immense.
Leaving aside what it can do in the future, let us shift our focus to how it is already transforming banking and financial systems around the world. You do not need to take our word for it. Have a look at this techemergence.com article to see how heavily the top 7 U.S. banks have invested in AI.
Case in point:
J.P. Morgan Chase recently introduced Contract Intelligence (COiN) platform to analyse legal documents and extract relevant information from them. Prior to adopting COiN, manual review of 12,000 documents used to take up nearly 360,000 hours of work. Post its introduction, they get the same work done with superior accuracy in a matter of seconds!
The article is full of such examples that have saved banks an astronomical number of hours. Unlike automation, which would take the same time processing a particular job any number of times, AI follows a self-learning approach. It keeps learning based on the data that it has consumed and would use previous experience to improve its own performance.
Here are some of the areas in banking and finance that AI can disrupt.
Banks are investing a fortune in enhancing their digital capabilities and offering personalized banking experience to clients at the click of a mouse through chatbots. Goldman Sachs sees Artificial Intelligence and Machine Learning as an opportunity worth $26 bn to $33 bn in terms of saving costs and adding new revenue streams.
Online trading has become mainstream, and markets around the world are attracting investors from all walks of life, i.e., salaried, High Net-worth Individuals (HNIs), Institutional Investors and many more. Millions of transactions are taking place globally in a mouse-click. The sheer volume presents a monumental challenge for monitoring scrutiny purposes.
However, AI has got this covered.
Different AI techniques such as Machine Learning and Deep Learning are being used globally to monitor HFT and to flag suspicious transactions in a matter of seconds. Brian Carter and Ryan Shreck of Vega AI Solutions, in their white paper for AI in High-Frequency Trading (HFT) have given us a glimpse into the immense potential of Machine Learning in HFT.
Banks around the world are focused on customer acquisition and customer service. Given the ever-growing customer base and competition, a number of banks worldwide have deployed chatbots to enhance customer experience and improve serviceability.
The moment you land on a website, you are greeted warmly through a chat window that pops up. It captures your attention immediately and helps you with the information you are looking for. AI has taken chatbots to a new level, where they can now help you pay your bills, fetch your account details, raise a service request and perform many other functions.
In less than a decade, chatbots have covered a fascinating journey from a simple digital tool used to perform repetitive customer queries to AI-powered digital assistants that can think and execute tasks on their own. Price Waterhouse Coopers provides a deeper insight into how chatbots are transforming banking globally and what the future has in store.
Banks all around the world are facing a challenging problem which grows bigger by the day. On the one hand, they have to simplify transactions for customers through various channels and, on the other, they need to keep thinking ahead to continually upgrade fraud-detection measures.
Old fraud detection measures were largely statistical, where programs would analyse heaps of data and raise a flag if there was a potential fraud. In addition to being cumbersome, this approach required tweaking the program with changing times, as transactions expanded from branches to websites to IVRs to smartphones and tablets. Fraud detection capability was further hindered by limited computing power.
New age banking has tackled this problem, thanks to affordable computing power that can be fully utilized by AI-powered, self-learning programs that improve in efficiency and effectiveness with time. TheBanker.com profiles a number of top bankers from around the world and tells us how Gill Wylie, Chief Operating Officer (Group Transformation) of U.K. situated Lloyds Banking Group is leveraging AI to nip fraudulent transaction menace in the bud:
“We use models that can detect when the person logged in to our online banking is not the customer, but rather a fraudster, or even a ‘bot.’ This helps us stop the fraudsters in their tracks.”
Risk Management has been an integral part of the global banking framework. Over the years, analysts and bankers have relied upon data dumped on spreadsheets to determine risk. As financial institutions continue to delve deeper into the digital world, data is being collected at a breakneck rate – much faster than what we can clean, transform and process.
Enter Artificial Intelligence, and things get easier, more efficient and accurate. AI solutions built on the platform of cognitive computing use Natural Language Processing to mine unstructured data, detect pattern and flag outliers. International Data Group study carried out in 2015 revealed that approximately 90% of data being generated in today’s world is unstructured. Nevertheless, it is a potential goldmine of information which, when handled correctly, can turn the practice of risk management upside-down by adopting a proactive approach rather than a reactive one.
Financial services industry faces ever-growing scrutiny in a connected world economy.
There is a monumental amount of data being generated every minute that needs real-time monitoring to detect insider trading, money laundering, market manipulation, foreign regulations compliance, etc. AI-powered systems can perform analysis in a matter of seconds and raise a red flag wherever applicable.
It would still need human intervention to eliminate false positive cases, but the system would save thousands of manual hours by automating routine calculations. It allows humans to focus on more sophisticated, strategic concerns.
Financial advisory is a high margin, immensely competitive industry and, unsurprisingly, the stakes are really high for every decision that is made.
Banks and financial institutions are jostling to acquire and retain wealth management customers from around the world, and they could do with every bit of competitive edge that they can get to stay ahead of the competition.
Robo-advisors have already made their way into the financial world. They can perform complex functions such as risk profiling to portfolio recommendation in a matter of seconds. As more and more data is fed to the system, AI-powered advisors would learn and mature, enhancing their accuracy over time.
Accenture blogs foresees AI to become full-fledged investment managers in the future, with present advisors assuming the role of relationship managers, having a 360-degree view of client data at their fingertips.
Vittorio D’Orazio, Research Director at Gartner, foresees a digital revolution in the world of banking and finance. In an interview given to etcio.com, he predicts that the financial industry’s decentralization will culminate into a complete transformation from the present monolithic, centralized set-up which dominates the markets.
“Also the economic power index will change as a result of the new world order. We have always seen a concentration of financial services activities in the financial hubs like New York, London, Frankfurt, and Tokyo. In the new world, this will change. New financial hubs will come up in India, China, Brazil, and Indonesia.”
By 2030, financial institutions around the world would have to adapt to a decentralized, programmable, blockchain-powered global economy where customers will be micro, opaque and not necessarily human!
If you want to start the journey towards that future, you do not want to miss the bus.
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