Public Funding for Natwest Group PLC

Customer satisfaction within the banking, financial services, and insurance (BFSI) sectors is experiencing a consistent decline. One promising solution lies in Large Language Models (LLMs), like the technology behind ChatGPT. Unlike traditional conversational Artificial Intelligence (cAI) systems, LLMs offer more natural interactions without requiring explicit instructions or manually labeled data. This makes them potential game-changers for customer service in the BFSI sector. However, there are challenges to consider: 1. Due to the sensitive nature of customer data, regulated institutions, such as our partner Natwest, cannot utilize public APIs like OpenAI to access LLMs. This is because of the associated regulatory, compliance, cybersecurity, and reputational concerns. 2. The establishment of in-house LLMs necessitates teams of ML experts and specialized infrastructure. This is problematic given the service sector's existing gaps in both AI proficiency and infrastructure With this project, we aim to develop a self-hosted LLM solution tailored for customer service operations. This will empower BFSI companies, regardless of their size, to harness this potent technology in a manner that's both secure and compliant.

Advances in Deep Learning have made Large Language Models (LLMs) a potential game-changer for customer service, as well as other commercial applications. These models can generate natural interactions without explicit instructions or manual data, unlike current conversational Artificial Intelligence (cAI) systems. However, this technology carries the risk of incorrect or fabricated answers, known as 'hallucinations', which can undermine customer trust and harm a company's reputation. In this collaborative project, the consortium consisting of Algomo, YuLife, Natwest and the Universities of Edinburgh and Essex will research and develop technologies that will identify, monitor and address hallucinations for customer service applications. This project fits Algomo perfectly, as it improves upon our core product, a cAI platform, and addresses the market need for trustworthy and reliable generative cAI. By developing a technology that effectively addresses hallucinations, we will increase trust and confidence in the outputs of LLMs, ultimately accelerating their commercial adoption not only for customer service but also for a variety of other use-cases.