README.md

metadata

title: AML Shield
emoji: 🛡️
colorFrom: blue
colorTo: red
sdk: streamlit
sdk_version: 1.32.0
app_file: app.py
pinned: false

AML Shield 🛡️

AI-Powered Anti-Money Laundering Transaction Intelligence Platform

A production-grade, full-stack web application for financial transaction intelligence. AML Shield allows users to upload transaction records and receive real-time anomaly detection, interactive visualizations, KYC risk profiling, and AI-generated compliance reports via LangChain + Bytez utilizing LLaMa 3.1.

All analyses are natively persisted to a Supabase PostgreSQL backend, displaying dynamic real-time insights globally.

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🚀 Live Demo

Access the live application hosted on Hugging Face Spaces:
👉 AML Shield Live Space

⚙️ Core Architecture & Features

This platform employs a Two-Layer Detection System utilizing rule-based flagging and Machine Learning models to score unverified transactions.

1. Upload & ETL Engine: Rigorous CSV validation routines parse structure variables and engineer critical analytical features natively.
2. Two-Layer Threat Detection: Applies known heuristics (Structuring, Dormant Account Spikes, etc.) and scikit-learn IsolationForest anomaly algorithms.
3. Advanced Risk Profiling: Consolidates activity for all accounts tracking behavioral metadata to derive accurate KYC tiering (KMeans).
4. LangChain AI Compliance Integration: Streams professional regulatory reports via langchain_bytez mapping direct analysis metrics to executive summaries in real-time.
5. Report Generation Pipeline: Leverages ReportLab building formal, fully-styled PDF analytics documents dynamically available for analyst download.
6. Persistence & Data Aggregation: Syncs outputs to Supabase capturing global macro-risk signals to identify organizational trend metrics.

🛠 Tech Stack

• Frontend Dashboard: Streamlit
• Data Modules: Pandas, NumPy
• Machine Learning models: Scikit-learn
• Analytical Storytelling: Plotly
• AI LLM Generation: LangChain, Bytez (meta-llama/Llama-3.1-8B-Instruct)
• Persisted Memory Layers: Supabase DB (supabase-py)
• Artifact Generation: ReportLab PDF renderer

🔧 Installation & Local Setup

1. Clone the repository:

git clone https://github.com/AJAYKASU/aml-shield.git
cd aml-shield

2. Setup Virtual Environment & Dependencies:

python -m venv venv
source venv/bin/activate
pip install -r requirements.txt

3. Configure Environment Variables: You will need API keys for Bytez and your Supabase PostgreSQL cluster. Add the following inside a .env file at the root:

BYTEZ_API_KEY=your_key_here
SUPABASE_URL=your_supabase_url
SUPABASE_KEY=your_supabase_anon_key

4. Run Project:

streamlit run app.py

🔐 Compliance Methodologies

The rules modeled directly reference core regulatory operations monitored by US analysts covering entities under the BSA (Bank Secrecy Act), FinCEN SAR requirements, and the FATF Recommendation 16 for wire transfer rules.

Built to demonstrate robust AML compliance analytics skills for data-driven financial services roles.