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🧞 Genie – Financial Operating System

Turn raw financial data into clear, actionable insights using an open-source Agentic AI financial copilot.

Python FastAPI AI Open Source

Genie is an open-source AI-powered financial assistant that helps users understand their financial behavior and make better decisions.

Most finance apps only show transactions and charts.

Genie goes further by answering the real question:

"What should I do with my money?"

Using Agentic AI, Genie analyzes financial data, identifies patterns, predicts future cash flow, and generates clear recommendations.

Think of Genie as your AI-powered financial copilot.

✨ Why Genie?

Financial data is everywhere:

  • bank transactions
  • subscriptions
  • payments
  • expenses
  • budgets

But turning that data into real financial insight is still difficult.

Genie solves this problem by building an AI system that understands financial behavior and suggests actions.

Instead of dashboards that only show numbers, Genie provides:

  • insights
  • predictions
  • recommendations

🚀 Features

🧠 Financial Insights
Automatically analyze spending and financial habits.

📊 Spending Pattern Detection
Understand where your money is going.

🔮 Cash Flow Forecasting
Predict future balances based on past transactions.

🚨 Anomaly Detection
Identify unusual or suspicious spending.

💡 AI Financial Recommendations
Get suggestions to improve budgeting and savings.

📈 Interactive Dashboard
Visualize insights through charts and reports.

⚙️ How Genie Works

Input

Genie receives financial data such as:

  • Transaction history (CSV or APIs)
  • User financial goals (budget, savings targets)
  • Questions like:
    • Where am I overspending?
    • How can I save more money?

Processing

Genie runs an Agentic AI workflow that:

  1. Processes financial transaction data
  2. Detects spending patterns and trends
  3. Identifies unusual financial activity
  4. Forecasts future cash flow
  5. Generates recommendations using AI models and financial rules

Output

Genie produces insights such as:

  • spending summaries
  • savings recommendations
  • cash flow predictions
  • alerts for unusual spending
  • automated financial reports

🎯 Example Genie Output

Monthly Financial Insight Report

Income: $4,000
Expenses: $3,250
Savings Rate: 18%

Insights:
• Food spending increased by 22% compared to last month
• 3 unused subscriptions detected ($38/month)

Recommendations:
• Reduce food delivery spending by $120/month
• Cancel unused subscriptions
• Increase monthly savings to $900 to reach your yearly goal

🏗 Architecture Overview

User / API Request
        │
        ▼
Financial Data Processing
        │
        ▼
Agentic AI Analysis Engine
        │
        ├── Spending Analysis
        ├── Cash Flow Forecasting
        ├── Anomaly Detection
        │
        ▼
Recommendation Engine
        │
        ▼
Financial Insights & Reports

Supporting services:

  • PostgreSQL (financial data storage)
  • Redis (caching)
  • Vector database (knowledge retrieval)

🛠 Tech Stack

Backend

FastAPI (Python)

AI / LLM

LangGraph or LangChain
HuggingFace / OpenAI compatible models

Data

PostgreSQL
FAISS / Pinecone (vector database)

Infrastructure

Redis
Docker

Dashboard

Streamlit or React

📂 Repository Structure

genie/
│
├── api/            # FastAPI backend
├── agents/         # Agentic AI modules
├── analytics/      # Financial analysis logic
├── data/           # Sample datasets
├── services/       # Business logic
├── dashboard/      # UI / Streamlit dashboard
├── tests/          # Unit tests
└── docs/           # Documentation

🧪 Sample Dataset

date,description,category,amount,type
2026-01-01,Salary,Income,50000,credit
2026-01-03,Swiggy,Food,450,debit
2026-01-05,Rent,Rent,15000,debit
2026-01-06,Netflix,Subscription,649,debit
2026-01-08,Uber,Transport,230,debit
2026-01-10,Amazon,Shopping,1200,debit

🗺 Roadmap

Phase 1 — Core System

  • FastAPI backend
  • CSV transaction ingestion
  • Spending insights

Phase 2 — Financial Intelligence

  • Cash flow forecasting
  • anomaly detection
  • recommendation engine

Phase 3 — AI Layer

  • agentic workflow
  • LLM-powered insights
  • explainable recommendations

Phase 4 — Platform

  • dashboard
  • API integrations
  • report generation

🤝 Contributing

We welcome contributors from the community.

Ways to contribute:

  • financial analytics modules
  • AI insights
  • dashboard features
  • documentation
  • testing

Look for issues labeled:

good first issue
help wanted

👨‍🏫 Mentor

Pratik Dhanave

💬 Community

C2SI Slack
Channel: #genie

⭐ Support the Project

If you find Genie interesting, please consider starring the repository ⭐

It helps the project grow and reach more contributors.

🔗 Repository

https://github.com/c2siorg/genie