Ed Obrien
Ed O'Brien Sep 20, 2017

Clever leverage for customers: Enhanced experiences through AI and machine learning

As banks, credit unions, and financial services organizations look to better serve their customers, many seek new, innovative ways to understand wants, needs, and behaviors. Increasingly, this means the use of digital tools and transformation initiatives—as well as customer and predictive analytics, artificial intelligence (AI) and machine learning. All those high-tech means add up to a most human end: to better know and understand prospect and customer needs to provide an outstanding customer experience.

For many, initial exposure to AI has come through consumer electronics such as Apple’s Siri and Amazon’s personal assistant, Alexa. For others, this journey began via progressive retailers who’ve defined and executed on key elements of omnichannel retailing and “omnicommerce” (seamless customer experience through all available shopping channels). Along with Apple and Amazon, the likes of Best Buy and Nordstrom now exert relentless effort to better understand customers and construct customer-centric systems.

So what drives increasing customer interaction and engagement? More than ever, analytics and digital transformation efforts define the discussions. Often, organizations find the transformation is less about technology changes and more about customer expectation changes—and responding with enhanced business models and standout customer service.

Yet this poses a problem. To understand and anticipate expectations is more difficult than ever as businesses sit on massive amounts of big data—sometimes dozens or hundreds of terabytes of it, structured and unstructured—and no clear plan. It often resides in databases and data warehouses throughout these companies, sometimes in siloed, disparate systems. To sift through it requires AI, machine learning and predictive analytics techniques; this process can identify trends as well as predict customer wants, needs and behaviors.

Once the stuff of science fiction, these tools are now business fact—and increasingly essential, as McKinsey estimates that the volume of all data continues to double every three years, thanks to the sharing of information from digital platforms and wireless sensors across systems.

Many organizations now know that faster, more accurate ways to predict customer behaviors are critical. This leads to successful interaction and engagement with prospects and customers alike. Analytics, AI and machine learning can help realize these goals. Timely, relevant alerts and next-best-action suggestions can augment customer outreach efforts and improve overall customer experience across industries.

What’s more, AI and machine learning solutions can be found everywhere from the use of in-home chatbots to similar such tools in call and contact centers. The algorithms these solutions use “learn” in various ways, based on exposure to and interaction with data. And the more data processed—and insights gained—machine learning programs become more intelligent and adept to discover clearer patterns that lead to better predictions.

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.

Register for BAI Beacon, Oct. 4 and 5 in Atlanta, Georgia.

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 eobrien@athpower.com.

Editor’s note: Ed O’Brien will co-present "What Do Small Businesses Want in a Banking Partner: More Touch or More Technology?" , 2 p.m. Wed. Oct. 4 at BAI Beacon in Atlanta, Georgia. He will be joined by Jason Cohen, vice president of small business digital strategy at Wells Fargo.

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