Enterprise Agentic AI for Financial Compliance & Fraud Governance Overview
This project implements a governed multi-agent AI system designed to evaluate financial expense submissions within enterprise environments. The architecture combines retrieval-augmented reasoning, tool orchestration, fraud risk scoring, meta-evaluation, escalation logic, and immutable audit logging to simulate production-grade financial AI governance.Unlike standard RAG-based chat systems, this design prioritizes determinism, traceability, resilience, and structured decision-making aligned with real-world compliance requirements.
Problem Statement Financial organizations face operational and regulatory risk from: Misclassified expense submissions Fraudulent reimbursements Policy non-compliance Inconsistent manual review processes Traditional review workflows are reactive, difficult to scale, and challenging to audit.
This project introduces a governed agentic AI decision engine that: Grounds reasoning in retrieved policy documents Applies behavioral risk scoring Enforces structured escalation rules Logs every decision in an immutable audit trail
The objective is to simulate a production-ready compliance review system rather than a conversational AI prototype. System Architecture High-Level Flow: User Input → Planner Agent → Retrieval Agent (FAISS + Embeddings) → Compliance Reasoning Agent (LLM) → Self-Evaluator Agent → Controlled Retry Loop → Fraud Risk Scoring Agent → Escalation Engine → Immutable Audit Logger
Agent Responsibilities Planner Agent Determines which tools and reasoning steps are required based on expense attributes.
Retrieval Agent Performs semantic search over embedded policy documents to provide grounded context.
Compliance Reasoning Agent Generates structured decisions strictly grounded in retrieved policy clauses.
Self-Evaluator Agent Audits reasoning for grounding, hallucination risk, and logical consistency. Can trigger bounded retry.
Fraud Risk Agent Applies deterministic heuristics such as high-value thresholds and suspicious keyword detection.
Escalation Engine Escalates decisions when: Compliance outcome is rejected Fraud risk is elevated Confidence is low Grounding is insufficient Audit Logger Writes append-only structured JSON entries for regulatory traceability. Enterprise Governance Features Multi-agent orchestration with controlled execution Reflection-based retry loop with bounded limits Deterministic escalation enforcement Append-only audit logging (JSONL format) LLM timeout protection Safe fallback handling for malformed model output Structured response schema for downstream systems Failure-resilient logging that never interrupts API execution
Example API Response { "planned_actions": ["retrieve_policies", "compliance_reasoning"], "decision": { "decision": "flagged", "reasoning": "...", "confidence": 0.82 }, "evaluation": { "grounded": true, "reasoning_quality": 0.88, "needs_retry": false }, "fraud_risk": { "risk_score": 0.5, "risk_level": "medium" }, "retry_count": 0, "escalated_for_review": true }
Why This Is Not Just RAG This system extends beyond traditional Retrieval-Augmented Generation by incorporating: Planner-controlled tool orchestration Meta-evaluation of reasoning quality Autonomous but bounded retry correction Fraud scoring layer Deterministic escalation logic Immutable audit trace The architecture reflects production-oriented financial AI governance rather than a chatbot demonstration. Technology Stack FastAPI (API layer) FAISS (Vector similarity search) SentenceTransformers (Embeddings) Ollama + Llama3 (Local LLM)
Python
Structured JSONL audit logging