Skip to main content Skip to footer
Case study

The challenge

A large U.S.-based wealth management company was under pressure to reduce its contact center operating costs. Its existing operations had agents spending much of their day focused on responding to high frequency, low complexity requests, which was a drain on employee productivity and morale.

In addition to the cost pressure, the financial services industry was also undergoing a shift in customer expectations. Customers now expect to have answers to their questions quickly and efficiently online—avoiding a phone call whenever possible.

Our approach

Our client asked how we might leverage conversational AI to improve responses to common questions, reduce the workload on personnel and provide increasingly personalized service. We began by analyzing streams of data on high-volume call center inquiries to map flows for the most frequently asked questions. Next, we analyzed the various natural language processing platforms in the marketplace that would meet all the client’s requirements.

Our goal was to automate responses using keyword and pattern recognition driven by AI. Progressively accurate algorithms recognize words and phrases to identify a caller’s objective from a range of possible conversation flows. This dialogue was then cross-indexed to provide the best answers, while continuing to fine-tune the analytics. Our customer-facing virtual assistant automates inquiries and performs live agent transfers through text-based chat or by placing customers in a queue to receive a return call, depending on their preference.

Conversational AI reduces operating cost, call volume

This customer-facing virtual assistant automated over 400 of the client’s more common customer inquiries. It is capable of responding to both general questions and user-specific inquiries, with continuous improvement based on AI analytics and customer feedback.

$6.7 million

reduction in operating costs


fewer calls


improvement in customer experience index score