Five Ways in Which AI Is Changing Banking As We Know It

There was a time when every neighborhood bank in North America and Europe was acquired by or merged with a larger institution. By 2000, global mega-banks offered fewer choices to consumers looking for competitive interest rates and other services. But the too big to fail banks are now facing competition because of a resurgence of customer-friendly, local banks. There is an even bigger challenge: Technology companies have been applying for financial licenses that would allow them to enter the digital payments space.

As traditional banks grapple with the challenges posed by FinTechs, legacy constraints and traditional operational models, artificial intelligence (AI) is emerging as the savior. In a recent survey that Infosys commissioned on AI adoption across industries, 23% of the nearly 250 respondents, in the financial services sector, confirmed that AI technologies have been fully deployed in their organizations, and these are also delivering up to expectations; 47% of the respondents view AI as being fundamental to the success of the organization's strategy.

Here are five ways in which AI is changing the way banks and financial institutions are leveraging technology to engage and connect better with their customers.

  1. Intelligent digital assistants to amplify customer service
  2. As banking becomes an anywhere, anytime activity, Barclays chose to become even more responsive to its customers by developing an AI-enabled device to meet client's demand to transfer money. Embracing AI to simplify banking for its clients enables Barclays to offer some of the services which are staple at FinTechs.

    Then there's Nina, a Web assistant developed by Swedbank, a commercial bank serving customers across Sweden and the Baltic countries. According to officials at Swedbank, it's not uncommon for Nina to process 30,000 conversations focusing on 350 different queries each month. But the real proof of just how effective AI can be in improving customer service is that Nina had a first-contact resolution rate of 78 percent in the first three months of its operation.

  3. Data-backed lending decisions
  4. In summary

    Start-up lending platforms are opting for unconventional methods of credit score, beyond the traditional method, to lend, including information available from a person's online activities and interests. The online lender ZestFinance, which bills itself as a Big Data underwriter, utilizes advanced machine learning algorithms to price an applicant's personal credit risk. According to ZestFinance, its proprietary credit profiling system has improved the accuracy of their default predictions for one category of borrowers by 15 percentage points.

    While such P2P lending platforms are making it possible to include the traditionally unbanked population, they are also pushing traditional banks out of their comfort zones.

  5. Fraud detection through machine learning and pattern recognition
  6. It was five years ago (in 2012) that the prestigious trading firm Knight Capital Group had to assure its clients that all was well after a computer glitch caused a one-day loss of $440 million. Three years later, in 2015, the websites of companies as far ranging as United Airlines, the Wall Street Journal, and the New York Stock Exchange were all shut down for reasons that the institutions claimed involved maintenance. But many cybersecurity experts reckoned that the shutdowns were part of a coordinated attack.

    More recently however, banks are distinguishing themselves as places that are technologically sophisticated and capable of meeting the financial needs of digitally savvy customers, and they are doing this by putting security on top of the list. Some of the world's largest credit card issuers, HSBC and JP Morgan Chase & Co. among them, utilize AI to analyze buying patterns of their cardholders. Any anomalies are red-flagged, and preventive measures taken before a cyber-thief can do lasting damage. And it's about time too. A report by Forter and, the Global Fraud Index found that in the first quarter of 2016, $4.79 of every $100 in online transactions were considered at risk. That's up from $2.90 year-over-year.

    The answer to the cybersecurity problem, therefore, is for banks to increasingly collaborate with technology firms to identify and plug potential threats before they turn into a breach.

  7. Biometric identification through speech and image recognition
  8. HSBC uses biometric technology at data centers that can detect and recognize faces. There are other biometric technologies, such as retinal scans, that are becoming popular with financial institutions. Wells Fargo offers a stringent biometric authentication feature as enhanced security to their corporate clients. The solution involves the analysis of the whites of the eyes of the customer, including the unique red vein patterns before giving access to the banking app.

  9. Accelerated growth through digital channels
  10. While banks are turning to AI in a decisive manner to meet the needs of their consumers, they are also gearing up to address the competition from tech companies like Google and Apple, which offer payments systems. The investment bank Goldman Sachs has invested $15 million in the financial analytics company Kensho. The investment bank's analysts and arbitrageurs are able to receive real-time analytics through user-friendly dashboards and interfaces. Prior to leveraging Kensho, Goldman bankers had to spend time on researching companies and markets. Now those questions can be answered and analyzed with a couple of clicks.

    A financially-focused AI offering is Kasisto, which understands voice commands that are heavily laden with banking terms. The Web itself is where many of the most sophisticated AI-enabled banking services exist. Sites such as Betterment, FutureAdvisor (the result of investing giant Fidelity and brokerage house TD Ameritrade), Personal Capital, and WealthFront offer 'robo-advisory services' that ironically analyze data in order to offer extremely personalized wealth planning recommendations. Consumers are also finding that seamless digital connections to a bank's knowledge base can be attractive, personalized, and customized to their needs.

Throughout the financial services world, artificial intelligence, whether it is machine learning, deep learning, or a series of algorithms that can crunch an array of big data, is giving enterprises distinct strategic advantages. When a bank, brokerage house, lender, or payments system effectively uses AI, they run more efficiently and are able to connect more effectively with a segment of the population that will never be replaced by machines: their customers.