AI in Fintech: How PSB Builds and Measures RAG

PSB has laid out its approach to deploying AI in fintech through RAG: the architecture, the metrics, and the testing loop for tasks where mistakes are especially costly.

Why did PSB choose RAG over a plain neural network?

Because the main problem with LLMs is not just weak answers but confident mistakes. RAG reduces them: the model first looks up information in a trusted knowledge base and only then generates an answer.

What does PSB's RAG approach consist of?

An in-house knowledge base, vector search across documents, quality-control metrics, and regular manual review instead of blind faith in the model.

What is RAG in AI, and why is it needed?

RAG is a method in which the model first searches a trusted dataset (an internal knowledge base) for the relevant information and then generates an answer. This helps avoid the errors that standard language models often make.

Why do banks choose RAG over standard neural networks?

Because standard models often make confident mistakes. RAG combines AI with an in-house database, vector search, and regular manual review, which makes it more reliable for financial operations.

What is RAG in AI?

RAG (retrieval-augmented generation) is a method in which a neural network first searches a trusted knowledge base for data and then generates an answer, to avoid hallucinations and errors.

How do you deploy AI in a bank without errors?

Based on PSB's experience, you need a combination of an in-house knowledge base, vector search, quality metrics, and regular manual review of the results.

What is RAG, and why does AI need it?

RAG is an approach in which the model first searches a trusted dataset for information and then generates an answer. This addresses the main problem with LLMs: not just weak answers, but confident mistakes.