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Many banks, financial services organizations, and credit unions seek new and creative methods to better understand their customers’ needs, wants, and behavioral patterns. To provide a truly outstanding customer experience and to truly understand their prospects’ and customers’ needs, organizations frequently use digital tools and transformation initiatives, as well as using artificial intelligence (AI), customer and predictive analytics, and machine learning.

Many people were first introduced to AI through consumer products such as Amazon’s Alexa, or Apple’s Siri. For others, their initial experience with AI has been with progressive retailers such as Apple, Nordstrom, Amazon, and Best Buy.  These omni-retailer firms have established leadership roles for themselves by re-defining key elements of omni-commerce, and by optimizing the communications and interactions between customer, brand, and omni-retailer.  They have been diligent in obtaining data about their customers’ needs, wants, and behaviors, and in turn have developed customer-centric programs and systems that deliver personalized customer experiences based on analytics. 

At the center of discussions about increasing customer engagement and improving customer interaction, are initiatives in analytics and digital transformation. Counterintuitively, this transformation is most frequently less about technology change, but more about being able to predict changes in customer expectation, and reacting quickly with modified customer service, and optimized business models.

The ability to both understand, and to anticipate, customer expectation is quite challenging.  Some organizations store hundreds if not thousands of terabytes of customer data, which is located in disparate databases and systems, stored in various data warehouses and other locations throughout the organization. In order to effectively analyze this data, the use of machine learning (AI) and predictive analytics is required, to identify trends, and learn to predict customers’ needs, wants, and behavioral patterns.

In order to deliver on ever-rising customer expectation, many organizations are finding that they are relying on finding quicker and increasingly accurate methods of predicting customer behavior patterns, which drive successful engagement and interaction with prospects as well as customers,  Machine learning, AI, and analytics can be key elements in achieving goals, and these solutions can provide augmentation to customer outreach initiatives. Timely and context-sensitive alerts and suggestions can improve the customer experience from beginning to end, and across industries.

Contact centers are beginning to rely more heavily on AI and machine learning, including the use of chatbots and other tools.  These chatbots are programmed with algorithms that are designed to learn various things, such as drawing conclusions from the data with which they interact. The more data they process, the more insights they will learn, and the learning process becomes better and better at pattern discovery and making predictions based on analytics. 

The more prominent AI and machine learning examples are found in the retail banking, credit card, insurance, financial institutions, and wealth management services industries. Retail banking call centers frequently use chatbot technology, and robo advisers are frequently used in wealth management firms. (This is most often the case in mass affluent market segments.)

DBS Bank, of Singapore, started using chatbot tools in 2016, within its Digibank digital bank. Now, it is estimated that more than 80% of customer queries can be resolved with the use of these tools. 1  These chatbots use “conversational AI,” via voice and text, which helps customers enjoy a quick service experience.  Frequently, the customer does not even realize that they are interacting with a bot, and not a person.

Robo advisers are being used more and more, as they can offer automated investment and savings advice based on the unique goals and financial data of an individual or a family.  Rule-based algorithms and machine learning can suggest the next-best-action(s), and make recommendations that are based on industry best practices, are less expensive, and are personalized to meet the specific needs of customers.

To better serve prospects and customers, both personal interaction and machine learning are being used together, as a hybrid model. Morgan Stanley is a firm that relies on an enhanced human advising process, including machine learning that matches client risk tolerances and preferences to investment options.  This allows the financial advisor to discuss the appropriate investment possibilities for their clients.

Even though data management and analytics have provided significant value, there are always abundant opportunities to improve. The potential value estimates that came from using data and analytics are varied across industries. McKinsey estimates that the retail industry captured 30-40% of the potential value from these systems, while the manufacturing industry only captured 20-30% of potential value.

According to PwC, it is estimated that almost half of all manufacturing activities could potentially be automated, through RBA (robotic process automation).  In turn, this could become a $2 trillion global workforce cost reduction. 2 

In addition to widespread use in the manufacturing industry, RBA is also already in use for many other tasks, such as to process insurance claims, reconcile financial documents, and to resolve credit card disputes.

The potential to increase the future value derived from AI, machine learning solutions and analytics is almost unlimited, and can be realized in nearly all industries.  Improvements are possible in the areas of customer satisfaction, and in delivering a truly outstanding customer experience. Organizations are ill-advised to ignore the potential capabilities of AI and machine learning. 

1 http://www.americanbanker.com/news/chatbots-to-humans-move-aside-i-got-this

2 http://usblogs.pwc.com/emerging-technology/briefing-rpa/