Machine learning is transforming bank call centers
Machine learning and big data tools similar to those that power popular digital assistants like Alexa and Siri can enable banks and insurance companies to rationalize their operations and cost structures and, longer term, help gain insights about customer needs and identify new sources of incremental revenue.
Bank call centers have traditionally been focused on customer satisfaction by responding to routine requests for assistance at minimal cost. They have been run as cost centers, with average call hold time their key metric.
Machine learning (ML), Natural Language Processing (NLP) and Robotic Process Automation (RPA) help to develop and automate repetitive tasks and flows. Then Predictive Analytics facilitates building models that are not explicitly programmed. Instead, they learn interactively from each iteration of a problem--so they can make predictions and propose decisions. Machine learning models can find “hidden” patterns and trends in verbal and written call center interactions—including emotional sentiment. These key patterns can suggest next generation product and service innovation. They help to identify customer concerns and provide early warnings of problems.
Cheaper cloud-based data storage, automated processing and more powerful analytical tools to extract meaning from data are enhancing machine learning’s capabilities dramatically.
Machine learning tools can interpret nuance and support more customer-friendly responses for typical call center issues: for example, consumers asking about access to funds deposited or business customers asking about delays in wire transfers.
Since more than half of interactions with a call center are for routine issues, machine learning can be a starting point to develop self-service customer tools. It has been estimated that giving customers such tools would reduce call center volume--and cut call transfers by solving the problem at the first interaction—to save a typical U.S. bank with 1,000 branches $70-$80 million of potential savings.
Looking ahead, NLP and RPA promise to facilitate call centers creating software “robots” to use data and trigger chat responses to customer inquiries. These robots won’t mean the end of human call center agents. But they will enable agents to take advantage of potential sale situations to offer customers the right products and services.
Armed with machine learning capabilities, bank call centers are transforming into drivers of efficiencies, growth and profits. Forward-looking banks need to embrace this compelling opportunity.
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