London Stock Exchange Group Boosts Client Services with Amazon Q Business

London Stock Exchange Group has implemented Amazon Q Business to enhance its post-trade client services, aiming to provide quick and reliable access to information. The use of AI-powered assistants like Amazon Q Business can significantly improve efficiency and reduce the risk of miscommunication by instantly providing answers and aiding in navigating complex systems. This technology enables employees to be more creative, data-driven, and productive.

The London Clearing House (LCH) Group, a part of LSEG PLC, has been exploring ways to enhance customer support and increase the impact on customer success as part of LSEG’s AI strategy. LCH sought a managed conversational assistant that requires minimal technical knowledge and focuses on a knowledge base that can be easily updated. They decided to utilize Amazon Q Business, incorporating techniques like Retrieval Augmented Generation to streamline their client services.

LCH’s customer services team handles queries across various asset classes and products, necessitating access to detailed documentation to provide accurate advice to members. Traditionally, the team relied on FAQs and an in-house knowledge center. By exploring generative AI, they aimed to reduce customer queries, enhance the customer experience, and improve staff productivity by leveraging technologies like Amazon Q Business to provide quick and accurate responses.

The development of the AI assistant application involved ideation, knowledge base creation, integration, and testing. Through collaboration with Amazon Web Service (AWS), LCH successfully implemented a solution that improved response time, accuracy, and overall business performance. The application was rolled out in phases, with plans to integrate it further into existing interfaces and expand its usage within LSEG.

The solution overview of the LCH-built Amazon Q Business application showcased a web-based interface for internal client services teams to interact with the API and various AWS services. The architecture included components like Amazon ECS, Amazon API Gateway, AWS Lambda, and Amazon Bedrock, all secured using SAML 2.0 IAM federation. The application’s workflow encompassed data indexing, retrieval, validation, and real-time responses to user queries.

The validation process, which compared AI-generated responses against a golden answer knowledge base, helped build trust and confidence in the Amazon Q Business application. By providing quick and accurate responses, the AI-powered assistant not only enhanced customer experience but also improved staff productivity, demonstrating the value of generative AI in enhancing client services within complex and technical domains.

In conclusion, LSEG’s adoption of Amazon Q Business for LCH client services agents exemplifies the benefits of leveraging AI to streamline B2B query handling. By working backward from a business goal to improve customer experience and staff productivity, LSEG successfully implemented generative AI technologies in a regulated and data-intensive environment. This use case highlights the importance of managed, user-friendly AI solutions like Amazon Q Business in enhancing technical and business operations.