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🛡️ Building an Enterprise AML/CFT Risk Intelligence Platform with Gemma 4

A developer built Risk-Gemma4-Agent, an open-source AML/CFT risk intelligence dashboard that pairs automated transaction analytics with semantic compliance reasoning via the Gemma 4 model (gemma-4-26b-a4b-it) and LangChain vector RAG stores, aligned to the Reserve Bank of India's Master KYC Directions. The Streamlit application uses a modular, decoupled architecture that separates stateless analytical compute (gemma4_engine.py) from role-restricted presentation layers, with sidebar-based session authentication. The developer argues open-weight models let regulated banks keep sensitive customer transaction data off third-party cloud APIs.

read1 min views1 publishedOct 31, 2026

This is a submission for the Hacktoberfest Open-Source AI Challenge Week 4: Ghost in the Machine

I built Risk-Gemma4-Agent, an enterprise-grade Anti-Money Laundering (AML) and Counter-Terrorist Financing (CFT) risk intelligence dashboard panel. In the spirit of the Ghost in the Machine theme, this system acts as a high-vigilance digital watchdog that uncovers "invisible footprints" and hidden financial patterns—such as micro-smurfing networks and automated velocity layering—that would otherwise haunt institutional ledger networks undetected.

The application features a modular, multi-tab Streamlit architecture with an active, real-time workspace. It includes:

You can view the full decoupled system, codebase architecture maps, and Docker setup instructions here:

An enterprise-grade Anti-Money Laundering (AML) and Counter-Terrorist Financing (CFT) risk intelligence dashboard panel. This system couples high-performance automated data processing with deep semantic compliance reasoning via the Gemma 4 LLM architecture (gemma-4-26b-a4b-it) and LangChain Vector RAG stores, adhering strictly to the Reserve Bank of India (RBI) Master KYC Directions.

The platform is engineered using a modular, decoupled architecture separating stateless analytical data compute layers (gemma4_engine.py) from dynamic, role-restricted presentation layers ( app.py and tabs/). The global session management authentication loop is handled persistently through the left sidebar console.

graph TD
    %% Global Session Management Authentication Layer
    subgraph Sidebar Panel Session Gateway
        Auth[🔑 sidebar: Enter Gemini API Key] -->|Validates API Key| IAM_Gate{🔐 sidebar: Session Active?}
        IAM_Gate -->|iam_logged_in == False| Login[🔓 Choose Identity & Click Login]
        Login -->|Mounts session_state active_iam_user| Workspace_Unlock[🟢

… The platform separates stateless backend intelligence from dynamic presentation layers:

In highly regulated banking environments, financial entities are strictly prohibited from streaming sensitive customer transaction logs over public, third-party cloud APIs. Open innovation and open-weight architectures change the game entirely:

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