A Tier 1 Global Bank faced a significant operational challenge within its client services division. With a team of 230 representatives handling millions of trade exception queries - largely via an estimated 6 million annual emails - the bank’s processes had become heavily reliant on manual workflows. This lack of automation not only limited scalability but also hampered speed, visibility, and efficiency.

  • Client service agents were required to:
  • Extract trade data from long and fragmented email chains
  • Search across multiple internal systems to locate relevant trade records
  • Manually classify and route queries to the appropriate teams
  • Track resolution statuses across disconnected inboxes without unified oversight
  • Respond to client requests without any data-driven prioritization

These inefficiencies led to delayed responses, missed communications, and difficulty meeting service level agreements (SLAs).

To address these challenges, Capco partnered with the bank and Microsoft to launch a proof of concept (POC) for an end-to-end Client Service Automation solution. By leveraging Microsoft Dynamics 365, Copilot for M365, and Microsoft Power Platform, the initiative aimed to radically improve client service operations through the strategic use of Generative AI.

High-Level Approach

Capco collaborated closely with Microsoft and the client to support the design and delivery of a custom-built Dynamics 365 solution, enabling end-to-end client service workflow automation powered by Generative AI.

Capco’s contribution spanned strategic guidance, business analysis, and implementation support. Key activities included:

  • Defining the solution vision and scope including five core intelligent AI capabilities, a comprehensive product roadmap, and a Day One Minimum Viable Product (MVP)
  • Developing a robust classification framework for incoming emails by identifying 12 key query categories to drive automation logic
  • Designing intelligent routing mechanisms to direct queries to the appropriate teams and individuals based on complexity and ownership
  • Mapping critical data elements from client emails and internal systems to support API development and fine-tuned AI prompt engineering

This structured and collaborative approach laid the foundation for a scalable, intelligent client service platform capable of handling high-volume exception management with speed, accuracy, and insight.

Key Benefits

  • The solution could free up around 130,000 hours of staff capacity each year.
  • It led to a 20% faster average response time to client queries.
  • There was a 20% increase in resolving issues on the first attempt.
  • Dashboards were introduced to give managers better oversight and enable continuous improvement of the AI models.
  • The process is now more automated and easier to scale as demand grows.

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