What is Financial Risk And Compliance Manager A2a

The Financial Risk and Compliance Manager - A2A is a modular, multi-agent system designed to automate compliance analysis for financial institutions. It utilizes the Agent-to-Agent (A2A) protocol for inter-agent communication and the Model Context Protocol (MCP) server for standardized access to Generative AI tools, including Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs).

Use cases

Use cases include automating the analysis of regulatory documents, generating compliance reports, and providing risk assessments through a collaborative workflow involving multiple agents.

How to use

Users can interact with the system through a user-friendly Streamlit frontend, where they can submit compliance queries and view results. The system will then leverage its agents to analyze the queries against indexed regulatory documents and generate compliance reports.

Key features

Key features include a user-friendly interface, inter-agent communication via the A2A protocol, access to advanced Generative AI tools through the MCP server, and a collaborative workflow among specialized agents like the Profile Checker, Risk Reporter, and Compliance Querier.

Where to use

The Financial Risk and Compliance Manager - A2A is primarily used in the financial sector, particularly by financial institutions that require automated compliance analysis and reporting to adhere to regulatory standards.

Financial Risk and Compliance Manager - Comprehensive Documentation

🎥 Video Demo

Please see the Video Demo

Proprietary Software Notice

This software is proprietary and confidential. Unauthorized copying, distribution, or use of this software, via any medium, is strictly prohibited.

1. Project Overview

The Financial Risk and Compliance Manager with A2A Protocol and MCP Server Architecture is a modular, multi-agent system designed to automate compliance analysis for financial institutions. It leverages the Agent-to-Agent (A2A) protocol for inter-agent communication and the Model Context Protocol (MCP) server for standardized access to Generative AI tools, such as Retrieval-Augmented Generation (RAG) and Large Language Models (LLMs). The system indexes regulation documents, retrieves relevant context, and generates detailed compliance reports through a collaborative workflow involving multiple agents. This approach offers improved accuracy, efficiency, and scalability compared to traditional compliance systems.

🧩 Architecture

The system architecture consists of the following components:

• Streamlit Frontend: A user-friendly interface for submitting compliance queries and viewing results.
• MCP Server: A centralized server that provides access to Generative AI tools, such as Retrieval-Augmented Generation (RAG) using LangChain and FAISS, and Large Language Models (LLMs), using the Model Context Protocol (MCP).
• Agents: A set of specialized agents, including the Profile Checker, Risk Reporter, and Compliance Querier, that collaborate using the Agent-to-Agent (A2A) protocol to perform compliance analysis.
• Regulation PDFs: A collection of regulatory documents that serve as the knowledge base for compliance checks, indexed by the MCP Server’s RAG functionality.

🔄 System Architecture Diagram

sequenceDiagram
    participant UI as Streamlit Frontend
    participant MCP as MCP Server (Flask)
    participant Agent2 as Risk Reporter
    participant Agent1 as Profile Checker
    participant Agent3 as Compliance Querier

    UI->>Agent2: 1. Sends query (via MCP)
    Agent2->>Agent1: 2. Requests client profile (A2A)
    Agent1-->>Agent2: 3. Returns profile data (A2A)
    Agent2->>MCP: 4. Queries regulation context
    MCP-->>Agent2: 5. Returns context
    Agent2->>Agent3: 6. Sends risk report (A2A)
    Agent3->>MCP: 7. Queries compliance details
    MCP-->>Agent3: 8. Returns compliance response
    Agent3-->>Agent2: 9. Sends final report (A2A)
    Agent2-->>UI: 10. Returns result (via MCP)

Notes on the Diagram:

• A2A Communication: Direct Agent-to-Agent interactions (e.g., Agent 2 to Agent 1, Agent 2 to Agent 3) are shown using the A2A protocol.
• MCP Server Role: The MCP Server acts as a centralized hub, providing access to RAG services (using LangChain and FAISS) and LLMs. Agents interact with the MCP Server to retrieve regulation context and compliance details.
• Integrated RAG Services: The RAG functionality is embedded within the MCP Server, which indexes regulation documents and handles retrieval requests from agents.

2. A2A Communication Protocol

2.1 What is A2A Protocol?

The Agent-to-Agent (A2A) protocol is a standardized communication framework that enables autonomous agents to interact and collaborate with each other. In this project, we implement A2A protocol to facilitate seamless communication between specialized agents, each responsible for different aspects of compliance analysis.

2.2 A2A Implementation in This Project

Our system implements A2A protocol through:

1. Direct HTTP Communication

   • Agents communicate directly with each other using RESTful HTTP endpoints
   • Each agent exposes specific endpoints for receiving and sending data
   • Communication follows a standardized JSON message format

2. Agent Communication Flow

   • Profile Checker → Risk Reporter: Sends client profile analysis results
   • Risk Reporter → Compliance Querier: Transmits risk assessment data
   • Compliance Querier → Risk Reporter: Returns compliance analysis results

2.3 Agent Communication Matrix

2.4 A2A Protocol Benefits

• Decoupled Architecture: Agents can be developed and maintained independently
• Scalability: New agents can be added without modifying existing ones
• Fault Tolerance: Individual agent failures don’t affect the entire system
• Standardized Communication: Consistent message format across all agents
• Traceability: Each communication is logged with timestamps and metadata

3. Core Components

3.1 Frontend (app.py)

• Built using Streamlit
• Provides a user-friendly interface for submitting compliance queries
• Displays results in a modern, card-based UI
• Allows configuration of RAG settings and other parameters

3.2 MCP Server (mcp_server.py)

• Acts as a centralized hub for AI services
• Implements Retrieval-Augmented Generation (RAG) using LangChain and FAISS
• Manages access to Large Language Models (LLMs)
• Handles document indexing and retrieval

3.3 Agent System (A2A Protocol)

The system uses three specialized agents that communicate using the A2A protocol:

1. Profile Checker (agent1_profile_checker.py)

   • Port: 5001
   • Role: Analyzes client profiles from JSON files
   • Location: client/ directory
   • Purpose: Validates and processes client information

2. Risk Reporter (agent2_risk_reporter.py)

   • Port: 5002
   • Role: Coordinates the compliance analysis workflow
   • Communicates with other agents
   • Generates risk assessment reports

3. Compliance Querier (agent3_compliance_querier.py)

   • Port: 5003
   • Role: Performs detailed compliance checks
   • Queries regulatory documents
   • Generates compliance reports

3.4 Data Components

PDF Files (in data/ directory)

The system uses several regulatory documents:

• aml_bsa_manual.pdf: FFIEC BSA/AML Examination Manual
• aml_31cfr.pdf: 31 CFR Chapter X – BSA Regulations
• sec_iaa40.pdf: Investment Advisers Act of 1940
• sec_rule_206_4_1.pdf: SEC Rule 206(4)-1

These PDFs serve as the knowledge base for compliance checks and are indexed by the MCP Server’s RAG functionality.

JSON Files (in client/ directory)

• Contains client profile data
• Used by the Profile Checker agent
• Structured data for compliance analysis

4. Workflow

1. Query Submission

   • User submits a compliance query through the Streamlit UI
   • Query is sent to the Risk Reporter agent via MCP Server

2. Profile Analysis

   • Risk Reporter requests client profile from Profile Checker
   • Profile Checker analyzes the client JSON data
   • Results are returned to Risk Reporter

3. Regulation Context

   • Risk Reporter queries MCP Server for relevant regulation context
   • MCP Server uses RAG to find relevant sections from PDFs
   • Context is returned to Risk Reporter

4. Compliance Analysis

   • Risk Reporter sends data to Compliance Querier
   • Compliance Querier performs detailed compliance checks
   • Final report is generated and returned to UI

5. Technical Implementation

5.1 A2A Protocol

• Agents communicate directly with each other using HTTP
• Each agent runs on a different port (5001-5003)
• Communication is standardized and follows a specific protocol

5.2 RAG Implementation

• Uses LangChain for document processing
• FAISS for vector storage and similarity search
• PDFs are chunked and indexed for efficient retrieval

5.3 Configuration

• Environment variables for API keys
• Configurable RAG settings (chunk size, overlap)
• Modular design for easy extension

6. Setup and Usage

6.1 Installation

1. Clone the repository:

   git clone https://github.com/sachin0034/Financial-Risk-and-Compliance-Manager---A2A.git
   cd Financial-Risk-and-Compliance-Manager---A2A
   

2. Install dependencies:

   pip install -r requirements.txt
   

   Or, if using Poetry or pip-tools, use pyproject.toml.

6.2 Starting Services

1. Prepare regulation files:

   • Place your regulation PDFs in the data/ directory.

2. Prepare client profiles:

   • Place your client JSON files in the client/ directory.

3. Set environment variables (if needed):

   • For example, for OpenAI:

     export OPENAI_API_KEY=your-openai-api-key
     

   • You can also use a .env file.

4. Start the MCP server:

   python mcp_server.py
   

5. Start the agents (in separate terminals or as background processes):

   python agents/agent1_server.py
   python agents/agent2_server.py
   python agents/agent3_server.py
   

6. Run the Streamlit UI:

   streamlit run app.py
   

7. Access the application:

   • Open your browser and go to: http://localhost:8501

6.3 Service Ports

7. Configuration

7.1 File Structure

• Client profiles: Place JSON files in the client/ directory
• Regulation Data: Place in data/ directory
• RAG settings: Configure chunk size, overlap, and vector DB in the UI sidebar
• Environment variables: Use a .env file for API keys (e.g., OpenAI, Google Cloud)

7.2 Environment Variables

Create a .env file with the following variables:

OPENAI_API_KEY=your-openai-api-key