5 Ways Artificial Intelligence Is Already Transforming the Banking Industry
Having a high-level understanding of the goals big banks are looking to achieve with AI can provide food for thought and inspiration for digital entrepreneurs interested in fintech.
Artificial Intelligence (AI) -- and its growing impact on and applicability for individuals and businesses alike -- is one of today’s most widely discussed topics. From virtual assistants like Siri and Alexa, to chatbots created by Facebook and Drift, AI is having a significant impact on the lives of consumers.
A study from Statista showed that the number of consumers using virtual assistants worldwide is expected to exceed one billion in 2018. Additionally, a 2018 survey by Accenture projected that 37 percent of U.S. consumers will own a digital voice assistant (DVA) device by the end of 2018.
It is readily apparent how AI-powered technology is making inroads into everyday life through DVAs and other consumer products, but AI is also having a transformative effect on an industry that impacts virtually all consumers and businesses: banking. Here are five ways that AI is already transforming the banking industry.
Customer service automation
As natural language processing technology evolves, consumers find it increasingly difficult to distinguish between a voice bot and a human customer service representative. This stems from improved abilities on the part of voice and chatbots to resolve customer issues without human intervention.
The benefits to banks of customer service automation are obvious: AI could lead to significant cost reductions. A recent study by Autonomous predicted that AI could lead to 1.2 million jobs being cut in the banking and lending industry, resulting in up to $450 billion in industrysavings by 2030.
Despite the potential rewards customer service automation promises, banks and other businesses need to proceed with caution in relying too heavily on voice and chatbots. The popularity of GetHuman illustrates this: It's a website that connects consumers with human CSRs to resolve their issue. In fact, voice and chatbots often work best when augmenting rather than replacing humans. At a minimum, the option to speak to a human, if necessary, should be readily available.
Want an example of how banks are creatively employing AI to serve customers? The Swiss bank UBS, ranked number 35 globally for its volume of assets, according to Accuity’s August 2018 global bank rankings -- has partnered with Amazon to incorporate its “Ask UBS” service into Alexa-powered Echo speaker devices.
Ask UBS, which is aimed at UBS’s European wealth management clients, enables users to receive wide-ranging advice and analysis on global financial markets just by “asking” Alexa. “Ask UBS” also acts as a teaching resource, offering definitions and examples of acronyms and jargon related to the financial industry.
While Ask UBS can make a call from a UBS financial advisor to a customer’s phone upon request, it is not yet able to access individual portfolios, execute trades or perform other transactions; it can't offer personalized advice based on a client’s holdings and goals. According to the Wall Street Journal, the reason is primarily security and privacy concerns. More in-depth and personalized service through a DVA may not be far off, though. In the article, a UBS spokesman stated that the company's aim is to make “Ask UBS” and similar tools “secure, compliant, and trustable for clients.”
Banks have access to a wealth of customer data, including detailed demographics, website analytics and records of online and offline transactions. By utilizing machine learning to integrate and analyze information from multiple, discrete databases to form a 360-degree customer view, banks are better positioned to personalize products, services and interactions based on the behavior of individual clients.
According to James Eardley, global director of industry marketing for enterprise software giant SAP, “The next step within the digital service model is for banks to price for the individual, and to negotiate that price in real time, taking personalization to the ultimate level.”
While personalized pricing of this kind may only become prevalent in the future, banks are already utilizing AI-processed behavioral data to advise individual clients on appropriate credit and savings products, based on their goals and habits. Santander, the world’s 14th largest bank, measured by its current assets, even hosted a competition, with a prize of $60,000, on the machine learning crowdsourcing site Kaggle, encouraging data scientists to write code that better “pairs products with people.”
In the banking and payments industry, personalization extends far beyond marketing and product customization, into security. A growing number of banks are utilizing biometric data, like fingerprints, to replace or augment passwords and other forms of client verification.
A report by Goode Intelligence forecast that 1.9 billion bank customers will be using some form of biometric identification by 2021. The Guardian reported that U.K. bank Halifax even experimented with Bluetooth wristbands that identified a client’s unique heartbeat to authenticate account access.
In a widely discussed innovation to its popular iPhone, Apple has evolved its Face ID so that it now uses AI-powered facial-recognition technology to unlock the device as well as validate purchases using Apple Pay, its digital wallet service. As facial recognition and other biometric authentication techniques become more sophisticated and secure, they are poised to become increasingly commonplace.
One of the most promising applications of AI in banking comes from automating high-volume, low-value processes. In one example, reported by McKinsey, JPMorgan began using bots to process internal IT requests, including employees' attempts to reset their work passwords.
Up to 1.7 million requests were expected to be handled by the bots in 2017, doing the work of 40 full-time employees.
Pattern recognition and fraud prevention
The ability of AI to sift through massive amounts of data and identify patterns that might elude human observers is one of its greatest strengths. One area where this capacity is particularly relevant is in fraud prevention.
According to McAfee, cybercrime costs the global economy $600 billion. AI and machine learning solutions are being deployed by many financial service providers to detect fraud in real time. An additional benefit of improved fraud prevention technology is that legitimate activity is flagged as fraudulent less often.
According to Tech2, Mastercard was able to reduce “false declines” for its customers by 80 percent using AI technology.
The fintech revolution is still in its infancy, but alongside AI, it has already had a substantial impact on the way traditional banks do business. This presents digital entrepreneurs and investors with myriad opportunities for improvement.
According to CB Insights, the first quarter of 2018 alone saw a global record $5.4 billion in funds raised by VC-backed fintech companies. Having a high-level understanding of the goals big banks are looking to achieve with AI -- including customer-service automation, personalization, improved security, process optimization and pattern recognition -- hopefully provides food for thought and inspiration for digital entrepreneurs attracted to the fintech space.
Entrepreneur Leadership Network Contributor