README.md

metadata

title: AegisOpenEnv
emoji: 🏦
colorFrom: indigo
colorTo: gray
sdk: docker
pinned: false
license: mit

🏦 AegisOpenEnv: AI-Powered Financial Compliance Sandbox

AegisOpenEnv is a high-fidelity Reinforcement Learning environment designed for the Meta OpenEnv competition. It translates complex banking compliance regulations into a rigorous, text-augmented simulation for training autonomous financial auditors.

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🏛️ Why AegisOpenEnv?

Financial institutions screen millions of transactions daily. Traditional rule-based systems often struggle with "smurfing" (structuring transactions just under reporting limits) or adapting to new Sanctions Lists.

AegisOpenEnv allows LLM-based agents to:

• Audit Raw Transactions: Process complex histories and account metadata.
• Reason with Regulations: Dynamically fetch and cite clauses like the EU AI Act or BSA.
• Learn from Feedback: Use modular reward signals to optimize for high precision and low false positives.

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🛠️ Task Catalog

Our environment features a 3-tier difficulty system to evaluate various auditor competencies:

Phase	Task ID	Name	Difficulty	Competency Evaluated
I	easy_audit	Sanction Check	🟢 Easy	Blacklist matching and deterministic identification.
II	medium_audit	Smurfing Detection	🟡 Medium	Pattern recognition across temporal windows.
III	hard_audit	Regulatory Alignment	🔴 Hard	Legal reasoning and precise clause citation.

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👁️ Environment Specification

📝 Action Space (AuditAction)

Agents respond with structured JSON containing:

• action_type: APPROVE, FLAG, or REQUEST_INFO.
• target_id: The identifier of the account or transaction under review.
• regulation_citation: A direct citation of the violated regulation (Required for Hard tier).

👁️ Observation Space (AuditObservation)

Agents receive:

• transactions: Real-time transaction flux.
• account_metadata: Profile data (age, tier, risk level).
• retrieved_regs: Dynamic context window containing regulatory guidelines.
• reward: The score from the previous action.

🎯 Reward Structure

AegisOpenEnv prioritizes Zero-Tolerance Compliance:

• Successful Audit: +0.5 to +1.0 (Identification + Citation).
• False Positive: -1.0 (Inefficiency penalty).
• Missed Detection (False Negative): -5.0 (Critical regulatory failure).

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🚀 Quick Start

Installation

pip install -r requirements.txt

Local Validation

# Start the server
uvicorn app:app --port 7860

# Run OpenEnv validate
openenv validate http://localhost:7860

Inference Baseline

Ensure you have set your API credentials in your terminal session:

$env:OPENAI_API_KEY = "your-api-key-here"
$env:API_BASE_URL = "https://openrouter.ai/api/v1"
$env:MODEL_NAME = "stepfun/step-3.5-flash:free"
$env:ENV_URL = "https://armaan020-aegisopenenv.hf.space"
python inference.py

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🏁 Compliance Status

This environment is 100% Compliant with the Meta OpenEnv specification.

• Validation URL: armaan020-aegisopenenv.hf.space/health
• Repo Walkthrough: View walkthrough.md for training logs and REINFORCE results.

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