Artificial Intelligence: The Weapon of Choice in Banks’ Fight for Survival

With machine learning models that ascribe human intuition and intelligence to digital platforms, traditional financial institutions can slash costs, reduce manpower, improve the banking experience and stay competitive.

Banking consumers are demanding more individualized experiences as they become increasingly accepting of new technologies. In the era where tech giants like Google, Apple, Facebook and Amazon dominate, people have become accustomed to seeing personalized offers built on data that they voluntarily provide, and now it’s a common expectation.

This affords banks and credit unions the opportunity to meet their customers’ needs and set themselves apart. A few leading banks are doing just that — expanding on the artificial intelligence ​​system used by voice-powered devices like the Amazon Echo, Google Home and Apple’s Siri to improve service and enhance the customer experience. For instance, Barclays Bank is developing an AI system to let customers talk to a device and get information they need for vital transactions. And the Swiss Bank UBS recently announced that it is using robots on the trading floor to boost traders’ performance.

Other banks are looking at using AI to help customers make investment choices in a modeling approach similar to what UBS is doing for its traders. These special kinds of machine learning models are developed to ascribe human intuition, experience and intelligence — untethered from the actual humans who have traditionally managed assets — to digital platforms that can be placed directly in the consumer’s hands.

Behind these developments are machine learning algorithms that model the characteristics of consumer behaviors — for example, incomes and typical investments which can then be used to predict investment preferences and patterns of choice. The machine learning algorithm runs in the background while another engine handling “speech-to-text” gives advice.

While these algorithms can learn, the “machine” element does not make them self-sufficient and self-sustaining. They must be fed the right data to the right models at the right intervals, typically by real live human beings — “data scientists” who are now playing pivotal roles in the digital transformation strategies of traditional financial institutions.

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One area in which banks are arguably adopting AI even faster is the management of unstructured data from customers — emails, news articles, legal documents and recorded telephone conversations. Analyzing this data starts with managing the data, before applying analytics. Only after that can AI be used to process information intelligently.

For example, JPMorgan Chase recently introduced a “Contract Intelligence” (COiN) platform designed to “analyze legal documents and extract important data points and clauses.” A manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours. But results from an initial implementation of this machine learning technology showed that COiN could review the same number of agreements in seconds, demonstrating the tremendous power of AI. With the potential to slash costs, reduce manpower and save thousands of hours, it should be no surprise that JPMorgan Chase is now exploring a myriad of other ways it can implement this potent new tool.

Smarter Banks With AI

AI has potential to make banks exponentially smarter. “Smarter” in this case means delivering better customer insights and intelligence, and thus a better customer experience — something most in the banking industry now believe is the key to differentiation, growth and increased profits.

For example, by studying and learning the behaviors of market participants (any market, not just the equities markets), AI could be used to learn how those markets behave, enabling better risk assessments. In the real world, an AI system that has learned the behavior of a particular trader and its effects on performance over time may help to prevent that trader from making unsuccessful decisions based on “gut feeling.” Modeling human behaviors — complex, emotional and influenced by a wide range of inputs — can also help bankers predict customers’ creditworthiness better than a credit score.

AI can also improve banks’ customer service. This can happen in several ways. AI can aggregate all information about a customer, so that it “knows” the customer, and can tailor its interactions. It is also conceivable that Apple’s face recognition software could play a role. The bank branch of the future may “recognize” me as I walk in, so that the consultant who greets me already knows about me.

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Since the global economic crisis in 2008 and regulations from Basel to Sarbanes-Oxley, cost pressures have been accumulating on banks. It has also created enormous potential for disruption. Now the question is whether the worm will turn, or if fintechs will become market leaders. Today, fintechs and banks are largely complementary to one another, but that could change… quickly. It remains to be seen how fast traditional institutions will realize the opportunities of digitalization — that is, not simply replacing analog processes with digital, but discovering completely new potential in data analytics and AI.

The biggest challenge is probably cultural. AI needs a “fail fast” approach, but banks still find it hard to accept failure. With AI, the financial world now has a way to give employees the freedom to start this cultural change.

Go on, dare to take that first step.

Based in Germany, Christian Engel is a business analytics advisor for SAS. His team recently conducted 100 interviews with business leaders to understand the current state of AI readiness in corporations.

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