Optimizing Customer Complaint Resolution in Banking

Explore how banks can overcome the time-consuming and expensive process of identifying and resolving customer complaints.

Gary Class
Gary Class
17. Mai 2024 3 min Lesezeit

Banks interact with their customers literally every minute of every day through a complex array of physical and digital channels, from branches and ATMs to phone calls, web platforms, mobile apps, and more. Inevitably, customers sometimes experience friction during these interactions. This not only results in a high volume of inbound phone calls and emails; it sometimes culminates in formal complaints filed with regulatory agencies. In fact, despite the best intentions of banks to improve their customer experience, the Consumer Financial Protection Board (CFPB) received 1.3 million customer complaints last year, 64% of which were sent to the bank named in the complaint for review and resolution after evaluation by the CFPB.  

Of course, resolving an issue to the customer’s satisfaction requires identifying the nature of the complaint and invoking the appropriate response. But unfortunately, determining the nature of a customer complaint is often a manual process—and therefore time-consuming and expensive. That’s because most interactions are captured as semi-structured or unstructured data and stored in silos—data that’s exceedingly difficult to access, integrate, and interpret. Insights into customer dissatisfaction and complaints, both implicit and explicit, are buried within a mountain of disparate data.  

To overcome this, a bank must leverage customer journey analytics to identify customer tasks, which are any activities within a bank-defined business process required for a customer to progress within their customer journey in pursuit of a financial goal. It’s critical to do this as early as possible in the customer’s channel interaction to promote adherence to the normative path (the optimal path for successful resolution of the task given the bank’s service delivery model). Customer tasks that fall too far off this path generate friction, customer dissatisfaction, and sometimes a formal complaint.  

To improve the customer journey, banks need to maximize customer task effectiveness (whether the task was resolved to the customer’s satisfaction) as well as customer task efficiency (the time and resources spent resolving the task). Maximizing the effectiveness of customer task resolution will directly increase customer retention, thereby enhancing customer lifetime value. Increasing the efficiency of customer task resolution will reduce the direct cost of customer service by reducing the frequency and duration of inbound phone calls.  

Of all the customer tasks on a customer’s journey, complaints are the hardest to resolve. A customer who registers a complaint is often emotionally discombobulated and unable to articulate the root cause of their concern in a way that allows the bank to formulate an appropriate response or mitigation strategy. Moreover, many complaints stem from unsuccessful resolution of the customer tasks that are the most challenging and complex for the bank to address in a timely manner.  

Fortunately, advances in the effectiveness, efficiency, and availability of large language models (LLMs) provide a more robust and cost-effective approach to triaging customer complaints. Teradata’s Bring Your Own Model (BYOM) functionality within ClearScape Analytics™ enables state-of-the-art LLMs to interrogate inbound customer communications to identify complaints, determine the nature of a complaint (via topic modeling), and determine the customer’s “sentiment.” The results of the LLM’s evaluation can be appended onto the customer record to more accurately assess the risk that the customer will close their accounts at the bank.  

To further enhance the interrogation of customer communications, generative AI can be used to generate logically composed, concise summaries of complaints. These summaries can then be disseminated to the downstream knowledge management applications used by bank representatives to document and, ultimately, respond to complaints.

Deploying LLMs to evaluate customer communications and automate the classification of complaints enables strategies to decrease the cost of responding to customer complaints. Plus, banks can reduce the number of complaints escalated to regulators like the CFPB and the corresponding risk of penalties for noncompliance. 

Ready to see how Teradata’s Customer Complaint Analyzer With Generative AI Solution Accelerator can maximize the effectiveness of customer complaint resolution and increase customer lifetime value?

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Über Gary Class

Gary is an accomplished industry strategist with extensive experience in financial services, where he has made significant contributions to advanced analytics and AI. Gary spent over three decades at Wells Fargo Bank as the Director of Advanced Analytics at the forefront of innovation during the transformational era of “anytime, anywhere” banking. His visionary leadership has shaped the landscape of financial services through innovation, data-driven insights, and strategic thinking.

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