Picture this: your CEO asks for the latest projections on your Q1 business reports. There’s a lot riding on the report’s accuracy, including your reputation with leadership. Would you trust a standard AI tool, like ChatGPT, to pull this report together?

While hundreds of millions of users have flocked to ChatGPT, including 92% of Fortune 500 companies, AI tools pose a considerable liability for FIs when used for operational work and decision-making.

That said, there is tremendous untapped potential for GenAI in banking that goes beyond customer chatbots. Institutions like Chase are already forging paths to use GenAI for in-depth data analytics and reporting.

What makes banking-specific GenAI more feasible are the emerging frameworks like Retrieval-Augmented Generation (RAG) that can solve the usual challenges of accuracy, reliability, and compliance.

When a traditional Large Language Model (LLM) generates a response to a prompt, it relies only on the data it was trained on. The information may be outdated, incomplete, or lacking in specific insights, leading to inaccurate information passed to the user. RAG enhances this process by introducing external information from trusted sources that adds more context to the response.

A GenAI tool with RAG is a nice-to-have for general users but a must-have for lenders. The gap between generic AI outputs and precise, business-critical insights directly impacts loan performance analysis and an effective competitive strategy.

By leveraging RAG-enhanced AI, lending executives can make more informed decisions, ensuring increased accuracy and a strategic edge in the market.

How RAG supercharges AI in three phases

Phase 1: After receiving a prompt, RAG searches for the latest data from reliable internal knowledge sources, such as company databases or a cache of documents, to retrieve information relevant to the prompt. 

Phase 2: Then, the retrieved data is fed back into the model to add context to the response being processed.

Phase 3: Finally, the model combines this new information with its pre-trained knowledge to generate a more accurate and context-aware response. 

By supercharging an LLM’s accuracy, RAG ensures that AI outputs are factually sound, up-to-date, and better suited for decision-making.

Optimizing GenAI for Lenders

FIs face challenges where success requires blending accuracy and transparency. If understood and used correctly, AI can assist lenders in staying ahead of market trends while observing prudential guidelines.

Lenders considering AI tools for performance analysis and reporting should qualify solutions based on their ability to:

• Understand lending-specific language – Ensuring that AI accurately interprets prompting, industry terminology, and relevant metrics.
• Minimize AI hallucinations – Reducing the risk of incorrect insights or miscalculations that could impact reporting.
• Leverage curated datasets – Avoiding references to non-relevant sources, ensuring insights are explainable and backed by reliable data.
• Support compliance-ready reporting – Generating outputs, verified by the user, that align with policy and regulations.

Conclusion

It’s important to make informed decisions when vetting AI vendors by having a basic understanding of frameworks like RAG. For financial institutions, the choice is clear: using traditional GenAI alone is too risky, while RAG-powered AI provides the accuracy, transparency, and adaptability required for banking and lending.