Highlights
- With the commoditization of the financial services industry, service quality has emerged as a key differentiator to maintain market position. Despite the proliferation of digital channels to enhance service, demand for customer support services has steadily increased.
- We believe that banks must deliver top notch service—the quality of service must be so high that customers should be able to perform all their banking activities without having to reach out to the contact center for support.
- A combination of GenAI, advanced analytics, and predictive modeling has the potential to change the face of customer service by enabling a shift from a reactive service model to a proactive one.
Introduction
In the highly commoditized financial services industry, service quality differentiates a financial institution from the pack of organizations competing for the custom of the same set of customers.
Service quality has a direct impact on customer satisfaction and retention. Outstanding service helps nurture long-term relationships, drives repeat business, and elevates the bank’s brand. Positive experiences lead to word-of-mouth referrals, expanding the client base, and solidifying market position.
Financial institutions utilize various channels to deliver customer service, including digital self-service and human-managed platforms such as contact centers and live chats. Customers have adopted digital banking channels at a rapid pace, but the volume of interactions handled by contact centers continues to increase annually. This trend has led to elevated operational costs and diminished customer satisfaction. In our view, the quality of service must be so high as to eliminate the need for customer support services—customers must be able to seamlessly meet all their banking needs without having to connect with a contact center agent.
The efficiency of contact centers is largely contingent upon the quality of agents handling calls and the underlying technology utilized for call management. To ensure successful problem-solving, it is crucial to accurately identify the issues that require attention and implement appropriate process improvements and technological interventions. Financial institutions frequently invest in digital channels, often failing to take a comprehensive approach to improving customer service and addressing issues in entirety. In our view, the way forward lies in adopting a solution backed by generative artificial intelligence (GenAI). GenAI has the potential to revolutionize customer support services by enabling a shift to proactive support through better self-service channels. In combination with advanced data analysis, predictive modelling, and real-time monitoring, GenAI can anticipate customer needs and offer personalized solutions, reducing the need for support services, in turn boosting service efficiency and customer satisfaction.
An action plan for success
The need for customer support should be an exception rather than the norm.
In our view, financial institutions must initiate change across the four dimensions of infrastructure and processes, the way support is offered, self-service digital channels, and contact center operations to enhance the quality of service and reduce the need for support.
Revamp existing infrastructure and processes
Customers typically seek assistance when their expectations have not been met. For instance, issues such as delayed payments, unrecognized charges in statements, or errors during mobile app login can prompt customers to contact customer support centers. Most of the issues that trigger a request for support are recurring in nature. By identifying and addressing the underlying causes of these issues, financial institutions can eliminate the need for customers to seek help.
GenAI can be leveraged to analyze extensive datasets to identify patterns in customer issues. By examining customer interactions, transaction histories, and feedback, GenAI can accurately detect common pain points and the underlying causes of recurring problems that trigger support requests. Armed with these insights, banks can initiate specific measures to permanently eliminate the root cause of such recurring problems (see Figure 1).
Figure 1: Strategy to mitigate customer support requirements
In our experience, such measures span streamlining systems and processes, improving workflows, increasing transparency, and enhancing user interfaces, among others. With GenAI, banks can share additional information about transactions and associated charges, provide a mechanism for customers to track their transactions in real-time, and send notifications on delayed payments—all of this will greatly reduce the need for customers to reach out for support services.
Shift from reactive to proactive support
The prevailing model in financial institutions necessitates that customers contact the helpdesk when they experience issues or require assistance, despite the fact that the triggers for these calls frequently exist within the institution's systems. This reactive approach implies that even when data suggests potential problems—such as atypical account activity, transaction failures, online or mobile login issues, and unexpected charges—the responsibility to initiate contact remains with the customer.
Integrating event-based triggers using predictive and GenAI can help financial institutions to identify specifics events which can cause distress to customers. Predictive AI analyzes these triggers to assess the likelihood of customers reaching out for support, enabling the institution to proactively anticipate and resolve issues (see Figure 2). GenAI can be used to develop a personalized solution based on the customer's profile and history in combination with standard operating procedures (SOPs).
Figure 2: Shift from reactive to proactive customer support
Increase the utilization of self-service channels
Despite the availability of online channels, many customers reach out to contact centers for support. This underutilization of digital platforms can be attributed to several factors including a lack of awareness about app features and the complexity of digital tools. Additionally, digital channels can be particularly challenging for older, less tech-savvy customers, who often prefer traditional communication methods. Furthermore, rule-based chat agents are limited in handling nuanced queries, which can frustrate users and drive them back to human-assisted channels (see Figure 3). These factors collectively hinder the adoption of digital channels, impeding efficient customer service in financial institutions.
Figure 3: Limitations of chat agents in offering efficient service
With GenAI, banks can tailor interactions to cater to the needs of individual customers. Utilizing advanced natural language processing (NLP) techniques, GenAI can transform chat agents into sophisticated virtual assistants that converse like humans and understand emotions, delivering personalized solutions, thereby reducing the need for contact with human agents (see Figure 4). Furthermore, integrating GenAI-backed chat functions into popular messaging platforms such as WhatsApp can increase adoption rates, as customers prefer familiar and convenient channels. This approach improves customer satisfaction and experience, reducing the volume of calls to contact centers.
Figure 4: Capabilities of sophisticated chat agents backed by GenAI technology
Enhance the efficiency of contact centers
Financial services contact centers encounter numerous challenges that impact both customer satisfaction and operational efficiency (see Table 1). These challenges underscore the urgent need for contact centers to adopt more advanced technologies, enhance training programs, and implement robust quality assurance measures to improve the overall quality of support. A comprehensive transformation approach will streamline operations by reducing average handling time (AHT) and increasing first contact resolution (FCR).
Challenge |
Solution |
Prolonged wait times leading to customer frustration and dissatisfaction. |
Advanced predictive AI to foresee the reasons for customer calls—agents gain a comprehensive view of recent customer interactions across various channels, allowing them to review customers’ attempts to resolve issues prior to calling the contact center. |
Low first call resolution requiring customers to make multiple calls.
|
GenAI-powered agent to offer recommendations and prescribe solutions based on SOPs and successful solutions used to resolve similar issues in the past. |
Poor customers experience due to lack of personalization and empathy in interactions. |
Real-time quality monitoring to track call etiquette, conversation speed, and empathy levels; a dynamic customer sentiment meter to evaluate customer's emotional state in real-time. |
Inconsistent agent performance due to insufficient training and lack of tools to address complex scenario. |
Access to detailed customer persona information and call transcripts, supported by a GenAI-powered co-pilot, to retrieve relevant data based on customer inquiries, suggest optimal actions, and offer prescriptive solutions. |
Inaccurate post call updates spanning summarizing on-call discussions, classification based on intent, and maintaining a record of complaints among others. |
GenAI to automatically document information received from the customer into CRM systems, summarize the call, tag the reason that triggered the call, and create complaint records, as appropriate. |
Table 1: Challenges encountered by bank contact centers and recommended solutions
While the benefits of GenAI adoption in financial services are undeniable, there are challenges as well—the BFSI industry report of the recent TCS AI for Business study revealed that ethical and responsible AI use is a top concern for GenAI adoption in financial services. Financial institutions must err on the side of caution—they must continuously monitor and refine AI models, ensure compliance with regulatory standards, and maintain transparent communication with customers regarding AI usage. In addition, financial institutions must establish adequate ethical guardrails to address issues related with biases and hallucinations. Data privacy and security too will need attention in the form of role based access controls.
100% call monitoring: A giant leap toward excellence
Contact centers monitor the quality of calls to enhance performance, improve customer satisfaction, identify the reasons for poor quality, and initiate remedial measures.
However, existing practices used to monitor call quality are often inadequate and inefficient. A small sample of calls—approximately 3-5%— are manually reviewed while the remaining 95-97% go unchecked. Such selective monitoring leads to missed opportunities to identify and rectify performance issues. Consequently, banks are not able to enhance the quality of agent performance and effectiveness of the contact center.
The existing monitoring process requires listening to entire calls to evaluate their quality, making it both time-consuming and costly. This not only increases operational expenses but also limits the scalability of quality monitoring efforts. As a result, numerous valuable insights and potential areas for improvement remain undiscovered, preventing the contact center from achieving optimal performance and consistently delivering high-quality customer support.
Achieving 100% call quality monitoring has thus far remained an aspirational goal for banks—but the advent of GenAI has changed this. GenAI has the capability to monitor each and every call rather than just a small sample, and in turn revolutionize the functioning of contact centers and the quality of support delivered This comprehensive monitoring allows identification of quality issues across all interactions, providing a complete picture of agent performance and customer experience. GenAI can automatically transcribe and analyze calls in real-time, detect nuances in tone and sentiment, as well as monitor compliance with regulatory requirements.
With end-to-end monitoring, contact centers can identify specific areas requiring improvement such as communication, adherence with protocols. Steps can then be taken to promptly address issues, optimize training programs, and enhance overall support quality. Additionally, it systematically documents interactions, thereby reducing the risk of non-compliance and associated penalties. This holistic approach not only improves operational efficiency but also elevates the standard of service, fostering greater customer satisfaction and trust.
Take the next step
The next-generation GenAI-driven contact center represents a significant advancement in customer support.
To maximize the benefits of GenAI in customer support services, financial institutions should adopt a strategic approach involving comprehensive needs assessment, investments in GenAI technologies, and strong data management practices. Having said that, the transformation journey will come with its own challenges but the benefits will be well worth the effort. Quick action is key, for first movers will gain an edge over their peers in the highly competitive financial services industry.
