Audit-ready RAG for fintech AI teams.
Evaluate fintech RAG answers for unsupported claims, policy violations, sensitive data leakage, and evidence gaps before they become production risk.
Example Risk
A model invents a promotional loan rate.
Generated answer
"The business loan rate is 5.5% for all customers this quarter."
HardRAG finding
Unsupported claim: retrieved policy does not include a universal 5.5% rate.
The problem is not only accuracy. It is defensibility.
Unsupported product claims
A model references rates, limits, eligibility rules, or fees not found in the retrieved policy.
Advice policy violations
A user asks for recommendations that cross internal or regulatory advice boundaries.
Customer data leakage
Retrieved account or identity context appears directly in generated output.
Weak audit trail
Compliance teams cannot reconstruct why an answer was approved or flagged.
Start in audit-only mode.
For financial services, the safest first deployment is observation: evaluate responses, collect evidence, and understand risk patterns before enforcing blocks.
Grounding review
Compare answers against retrieved policy, product, and disclosure context.
Privacy checks
Detect customer identifiers and sensitive data patterns before output is trusted.
Evidence package
Summarize findings in a format compliance and leadership teams can review.
Assess fintech RAG risk before production exposure.
Use a fixed-scope review to find the evidence gaps and controls your team needs next.