How I Built a Fintech AI Agent That Detects Fraud and Generates AML Risk Reports — With Zero Hallucinations

vijaya kumari — Fri, 22 May 2026 01:34:28 +0000

The Problem

AI in fintech cannot hallucinate.

A fabricated regulation reference = legal liability.
A missed fraud pattern = financial crime.
A wrong compliance answer = regulatory penalty.

Yet most AI demos ship without any eval layer.
I built one.

A 4-tab Fintech AI Agent deployed on HuggingFace:

Tab 1 — Fraud Detector
Analyzes transactions for fraud patterns.
Returns: risk score (0-10), red flags,
approve/review/reject recommendation.

Test input:
"Transfer $9,800 to Cayman Islands
at 3:47am from unrecognized device"

Result: 9/10 HIGH RISK → REJECT

Red flags caught:

Tab 2 — Compliance Q&A
RAG over hardcoded financial regulations:
KYC, AML, GDPR, SOX, PCI-DSS

Every answer:

Tab 3 — AML Risk Report Generator
Generates formal 6-section risk assessments:

Tab 4 — Eval Dashboard
Real-time metrics across all tabs:

Every Claude output is scored for:

{
  "faithfulness_score": 0.95,
  "confidence": 0.85,
  "hallucination_risk": "LOW"
}

This is the LLM-as-Judge pattern —
Claude evaluating Claude's own outputs.

Results from first run:

huggingface.co/spaces/Vijayarv07/fintech-ai-agent

GitHub:
github.com/vijayarjun7

Adding quantum-inspired compression to the
inference layer (QuantRot-PQC research).

Because reliable AI + efficient AI =
production-ready AI.