Retail banking hosts some of the more visible examples of AI and machine learning in action in wealth management, credit card and insurance lines of business. Chatbot technology in retail banking for call and contact centers is also on the rise, as is the use of robo advisers in financial institutions’ wealth management lines, typically in the mass affluent market segment.
DBS Bank in Singapore, which implemented chatbot technology within its Digibank digital bank in 2016, now estimates that it handles more than four in five customer queries in this manner. At DBS, and other institutions, the chatbot uses “conversational AI” to communicate with customers via voice and text. Customers not only enjoy a faster experience, but also have no clue they’re talking to a bot rather than a person.
Roboadvisers can provide automated savings and investment advice based on individuals’ unique goals and financial situation, as rules-based algorithms and machine learning suggest the next-best action. The resulting recommendations can cost less than human-based advice, with results based on industry best practices and tailored to meet a customer’s unique needs.
In some cases, machine learning and personal interaction join forces as part of a hybrid pro-customer model. Morgan Stanley offers an enhanced human advising process that includes matches investment options with client preferences and risk tolerances. This in turn informs financial advisors about investment possibilities to discuss with clients.
Benefits from AI and machine learning have impacted the retail and manufacturing sectors as well. For example, McKinsey found that over the past five years, U.S. retailer supply chain operations that adopted data and analytics solutions have seen operating margins increase up to a 19 percent.
Yet even with the abundant value generated by data management and analytics to date, ample opportunities exist to improve. The estimated potential value captured from the use of data and analytics has proven uneven thus far, with the retail industry capturing approximately 30-40 percent from such systems—and manufacturing even lower, at about 20-30 percent of potential value, per McKinsey.
Meanwhile, PwC estimates that almost half of all manufacturing activities might benefit from robotic process automation (RBA), which could translate into a $2 trillion reduction in global workforce costs as it takes over the mundane tasks of data entry and synthesis. The benefits extend to professions such as accounting. And RBA is already used to resolve credit card disputes, process insurance claims and reconcile financial statements, to name just a few tasks.
Looking ahead, tremendous opportunities beckon, with the promise of explosive value thanks to analytics, AI and machine learning solutions. These positives can impact almost every line of business and bolster customer satisfaction as they deliver outstanding customer experience. Companies that ignore the potential of these capabilities do so at their own peril—while companies that embrace AI and machine learning do so with a powerful pair.
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Ed O'Brien is EVP, research and strategy, for ath Power Consulting, a premier provider of research and customer experience solutions for the financial services industry. Ed can be reached at firstname.lastname@example.org.