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An AI agent that parses Visa and Mastercard rulebooks, predicts interchange fees per transaction and answers internal compliance questions grounded in card-network documentation.
Visa and Mastercard publish thousands of pages of interchange rules across dozens of PDFs. Compliance and pricing teams spent hours per week locating the specific clause that justified a fee category, and predicting interchange for non-standard transactions required tribal knowledge.
Generic LLM answers were unusable in a payments context: no source, no rule version, no audit trail. The team needed a system whose every answer could be defended in front of an auditor.
A retrieval-augmented agent that ingests the full Visa/Mastercard rulebook corpus, segments it by rule version, and answers fee-classification questions with paragraph-level citations.
A separate prediction module estimates interchange per transaction using card type, MCC, geography and transaction shape, with confidence scoring and an explicit "needs human review" fallback when the model is not confident.
PDF ingestion pipeline normalises rulebooks into a structured corpus keyed by network, region and rule version.
Hybrid retrieval (BM25 + dense embeddings) feeds an LLM agent that produces answers with mandatory inline citations.
A separate gradient-boosted classifier predicts the interchange category from transaction features. Both the retrieval grounding and the prediction confidence are surfaced to the user so they can decide when to escalate.
