Regulatory compliant LLMs for customer service in banking, financial services and insurance

银行、金融服务和保险客户服务领域合规法学硕士

• 批准号: 10091477
• 金额: $ 49.87万
• 依托单位: ALGOMO LIMITED
• 依托单位国家: 英国
• 项目类别: Collaborative R&D
• 财政年份: 2024
• 资助国家: 英国
• 起止时间: 2024 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=10091477
• 关键词: Regulatory; compliant; LLMs; customer; service

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 infrastructureWith 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.

银行、金融服务和保险（BFSI）行业的客户满意度正在持续下降。一个有希望的解决方案是大型语言模型（llm），就像ChatGPT背后的技术一样。与传统的会话式人工智能（cAI）系统不同，llm提供更自然的交互，而不需要明确的指令或手动标记数据。这使他们成为BFSI行业客户服务的潜在改变者。然而，有一些挑战需要考虑：1。由于客户数据的敏感性，受监管的机构，如我们的合作伙伴Natwest，不能利用OpenAI等公共api访问法学硕士。这是因为相关的监管、合规、网络安全和声誉问题。内部法学硕士的建立需要ML专家团队和专门的基础设施。考虑到服务业在人工智能熟练程度和基础设施方面存在的差距，这是一个问题。通过这个项目，我们的目标是开发一个为客户服务运营量身定制的自托管法学硕士解决方案。这将使BFSI公司，无论规模大小，都能以既安全又合规的方式利用这一强大的技术。