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

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在于大型语言模型(LLM)，如ChatGPT背后的技术。与传统的对话式人工智能(AI)系统不同，LLMS提供了更自然的交互，而不需要明确的指令或手动标记的数据。这使它们成为BFSI行业客户服务的潜在游戏规则改变者。但也存在需要考虑的挑战：1.由于客户数据的敏感性质，受监管机构，如我们的合作伙伴NatWest，不能利用OpenAI等公共API访问LLMS。这是因为相关的监管、合规、网络安全和声誉方面的担忧。建立内部低土地管理系统需要大规模土地管理专家团队和专门的基础设施。考虑到服务部门在人工智能熟练程度和基础设施方面的现有差距，这是有问题的。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做的自托管LLM解决方案。这将使BFSI公司，无论其规模大小，以既安全又合规的方式利用这项强大的技术。

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.