Generative AI-centred architectures for financial institutions
How to support AI-driven innovation in business and operations by adopting a reliable and scalable architecture.
#Generative AI
#Architectures
#Financial Services
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Financial institutions at the forefront of AI-driven innovation
Financial institutions, drawing on their extensive experience in utilising AI for various purposes such as fraud prevention and cybersecurity automation, have witnessed a significant shift with the emergence of Generative AI, which introduced a plethora of new use cases and impacts on daily operations.
To adapt to this trend, numerous financial institutions worldwide have initiated innovation programmes aimed at exploring Generative AI tools across different business domains. This has led to the recognition of the need for new architectures capable of integrating with existing systems and supporting "greenfield" projects enabled by third-party AI solutions.
The main components of a
Generative AI-centred architecture
Foundation Models and LLM
Foundation models underpin Generative AI, offering deep language insight from extensive pre-training. LLMs process text and capture dependencies well. Embracing evolving models with interchangeable capabilities and strategic integration enhances adaptability and resilience.
RAG and RLHF
Retrieval Augmented Generation (RAG) and Reinforcement Learning from Human Feedback (RLHF) enhance LLMs in finance by integrating external knowledge for accuracy and using human feedback for continuous refinement. These approaches improve model relevance and align outputs with business needs.
Agents
Agents manage tasks by breaking them down and delegating to Foundation Models for execution. Code Interpreters, specialized agents, autonomously generate code snippets for tasks, linking AI and programming to automate processes. These Agents and Interpreters adapt to uncertainties and challenges, akin to human problem-solving.
Telemetry and Guardrails
Financial institutions must meet standards of efficacy, reliability, performance, and compliance. Comprehensive Telemetry, collecting and analyzing data like traces and logs, is vital for system understanding and compliance. AI Guardrails, as ethical and accuracy sentinels, prevent undesirable outcomes and enable continuous improvement.
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The benefits of a Generative AI-centred architectural design
The architecture must be adaptable to diverse and potentially evolving use cases, satisfying stakeholders while ensuring observability and compliance. Based on Reply’s experience, creating an architecture independent of a single large language model proves advantageous. This concept of architecture ensures scalability amid the growing impact of Generative AI.
Flexibility is crucial, allowing for plugging and unplugging foundation models without disrupting the overall architecture. This approach also ensures stability, enterprise-grade performance, and security by design, enabling financial institutions to make flexible decisions based on business, security, and scalability.
