FaultAuditAI — Multi-Agent Corporate Finance Audit & Fraud Investigation System

Multi-agent AI system that investigates corporate finance fraud — with a human in the loop before anything consequential happens.

Live demo: https://www.twine.net/signin

The problem

Finance teams drown in transactions. Duplicate invoices, ghost vendors, split payments below approval thresholds and policy breaches hide in volume. Rules engines flag too much; a fully autonomous LLM agent is unacceptable when the action is “block this payment” or “escalate this vendor”.

What I built

A coordinator agent receives a case and dispatches specialist agents (duplicate detection, vendor legitimacy, policy compliance, risk scoring). Each specialist retrieves evidence via RAG over policy documents and transaction history, then reports back with citations. Findings are risk-scored, and anything actionable stops at a human approval gate. Every step is written to an immutable audit log, and the case is exported as a report.

Flow: request → coordinator → specialists → RAG evidence → risk analysis → human approval → audit log → report export

Why it matters

The design point was controllability, not autonomy. Approval gates, full audit trails and cited evidence are what make an AI agent deployable in a regulated finance function.

Stack

Qwen and Gemini models, multi-agent orchestration, RAG over policy and transaction data, PostgreSQL, FastAPI, report generation (PDF), audit logging.

Links

• Live demo: https://www.twine.net/signin
• Devpost submission and GitHub source available
• Architecture write-up: https://www.twine.net/signin

Solo AI engineering project, submitted to a public hackathon.