The banking industry has seen two major players, HSBC and Bank of New York Mellon (BNYM), commit to participating in the trial of Quantexa’s advanced AI technology suite, Q Assist. This decision is part of Quantexa’s Lighthouse Programme and represents a substantial advancement in the incorporation of cutting-edge AI tools within the financial sector. The debut of Q Assist at London Tech Week serves as evidence of the rapid progress being made in leveraging AI capabilities within the industry.
Q Assist, Quantexa’s latest innovation, has been unveiled almost a year after the company made a significant investment exceeding US$200 million in the global artificial intelligence industry. The AI suite is designed to enhance decision-making processes across various functions such as customer service, sales, and compliance within financial services, technology, media, telecom (TMT), and government agencies.
One of the primary advantages of the new Q Assist suite is its ability to operationalise generative AI with minimal infrastructure investment or the need for additional skilled resources. By integrating Quantexa’s Decision Intelligence Platform with generative AI, the suite provides more accurate and reliable data interactions across all data types within an organisation.
Q Assist utilises LLMs (Large Language Models) with Quantexa’s Decision Intelligence Platform to improve data understanding by anchoring responses in the rich context provided by Quantexa’s knowledge graph. This ensures the provision of an accurate and up-to-date amalgamation of structured and unstructured data, ultimately enhancing performance and trust in decision-making processes.
As a key participant in Quantexa’s Lighthouse Programme, HSBC has high expectations for the impact of Q Assist. The bank anticipates significant productivity gains within the first year of deployment, expecting streamlined analytics and investigation processes, thereby reducing dependency on data science and operations teams for ad-hoc requests. This will enable these teams to focus on more strategic initiatives.
David Rice, Global Chief Operating Officer of Commercial Banking at HSBC, expressed optimism about the potential of the new solution to streamline complex tasks such as anti-money laundering investigations and sales strategies by providing trusted data and contextual analytics. He believes that the introduction of contextual analytics and innovation will enable HSBC to allocate resources more productively and, ultimately, benefit their customers.
BNY Mellon is also considering participation in the Lighthouse Programme and has been working with Quantexa to project benefits over one, three, and five-year periods. Early findings suggest significant productivity gains across various areas, such as time-saving, efficiency improvements, new opportunity identification, and increased conversion rates.
According to Quantexa, a major financial institution employing Q Assist could realise annual savings exceeding US$17 million by enhancing and automating financial crime and fraud investigation processes. Jamie Hutton, Chief Technology Officer at Quantexa, highlighted the role of clients in shaping the product requirements for Q Assist, emphasizing the company’s focus on delivering maximum customer value.
Eric Hirschhorn, Chief Data Officer at BNY Mellon, lauded Quantexa’s continued innovation and the collaborative efforts that have helped break down data silos and unify data with unprecedented accuracy. He expressed excitement about the potential of enabling frontline workers across the bank to use Gen AI to act on data insights confidently and achieve new levels of efficiency.
The Q Assist suite comprises three main components: the Integration Layer, Prompt Builder, and Copilot. The Integration Layer serves as the framework for secure linkage between Quantexa’s Decision Intelligence Platform and various LLMs and conversational AI systems. The Prompt Builder allows administrators to define and control prompts and responses based on contextual data, while the Copilot enables real-time querying and summarisation of large and disparate datasets, aiding in research, investigation, and reporting tasks.
As of now, Q Assist is available to a limited set of customers, with plans for a broader public release in early 2025. This development signifies a significant leap in the integration of advanced AI capabilities within the banking industry, promising enhanced efficiency, accuracy, and innovation. The impact of this technology is poised to reshape the way banking operations are conducted, ultimately benefitting both financial institutions and their customers.