More and more, banking customers embrace new technologies and require increasingly individualized services. In the age of Google, Apple, Facebook and Amazon, we as customers have grown accustomed to personalized offers built on data we voluntarily provide—and now we expect it.
Other banks are considering AI to help customers make investment choices in a modeling approach that resembles what UBS does for traders. These special kinds of machine learning models are developed to describe human intuition and human operating experience, independent of a human trader or asset management consumer.
Behind this development, machine learning algorithms model the characteristics of the customer—for example, incomes and typical investments—and predict their preferred investment behavior and interests such as stock choices. The machine learning algorithm runs in the background while the engine responsible for “speech-to-text” gives advice.
While these algorithms can learn, the “machine” element does not make them self-sufficient and self-sustaining. They must receive the right models at the right intervals from a human—in this case a data scientist who’s become a vital part of the bank’s business department.
This by no means represents the only use of AI and analytics in banks. In fact, one area where banks are arguably adopting AI even faster centers on managing unstructured customer data: emails, news articles, legal documents and recorded phone conversations. Analyzing this data starts with managing it, then applying analytics. After that, AI can process this 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.” Manual review of 12,000 annual commercial credit agreements normally requires approximately 360,000 hours—an amount equivalent to 173 years. But results from an initial implementation of this machine learning technology showed that it could review the same number of agreements in seconds, demonstrating that COiN has widespread potential. JPMorgan Chase is now exploring additional ways to implement this powerful tool.
Artificial intelligence, smarter banks
AI also has potential to make banks smarter. That amounts to better customer intelligence and thus a better customer experience—a key to increasing profit. For example, as AI learns the behaviors of market participants, it can in turn learn how markets behave and enable better risk assessments. Modeling human behavior—complex, emotional and influenced by a wide range of inputs—can also help bankers predict customers’ creditability and their scores or ratings. An AI system that has learned a trader’s behavior and its effects on performance over time may help to prevent them from making unsuccessful decisions based on “gut feeling.”
AI can also improve banks’ customer service in several ways. It can aggregate all information about a customer so that it “knows” the customer and can tailor its interactions. Apple’s face recognition software could also 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.
AI as a survival tool
Since the global economic crisis in 2008 and regulations from Basel III to Sarbanes-Oxley, cost pressures have accumulated for banks. It has also created enormous potential for disruption. Now the question is whether the worm will turn or fintechs will become market leaders. Today, fintechs and banks predominantly complement each other. It remains to be seen how quickly the big players will realize the opportunities of digitalization—that is, not simply replacing analog processes with digital, but discovering completely new predictive and proactive potential in data sets 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. In time, AI-enabled machines will help us take the next, and many more to come.
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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