AI Agent Finance Team Powered Budget App

A full suite of Financial Analysis Team at the palm of your hand

FinBin is a financial planning API that helps users manage their budgets, track expenses, and save towards financial goals (called "buckets"). It integrates with Microsoft Semantic Kernel to provide AI-driven financial insights and savings recommendations while leveraging Azure SQL Database for secure and scalable data storage.

Quick Start (For Hackathon Judges)

1. Clone the repository

   git clone https://github.com/your-repo/FinBin.git
   cd FinBin

2. Update connection strings in appsettings.json

   • Set your database connection
   • Add your Azure OpenAI API keys

3. Run the migrations and start the API

   dotnet ef database update
   dotnet run

4. Access the API documentation

   • Open your browser to http://localhost:5263/swagger

Features

• User Management – Create and manage user profiles
• Income & Expense Tracking – Store and analyze financial transactions
• Buckets (Savings Goals) – Set up financial goals with target amounts and deadlines
• AI-Powered Financial Insights – Personalized analysis of income, expenses, and goal feasibility
• Automated Savings Recommendations – AI suggests adjustments to spending and savings
• Azure SQL Database – Secure and scalable cloud database storage

Tech Stack

• .NET 8 Web API
• Entity Framework Core (Azure SQL Database)
• Microsoft Semantic Kernel (Azure OpenAI GPT-4)
• Swagger (API Documentation)
• Azure App Services (For Future Deployment)

Setup Prerequisites

1. Install Entity Framework Core Tools

dotnet tool install --global dotnet-ef

If you already have it installed, update it:

dotnet tool update --global dotnet-ef

Verify installation:

dotnet ef

You should see the EF Core logo:

                     _/\__       
               ---==/    \\      
         ___  ___   |.    \|\    
        | __|| __|  |  )   \\\
        | _| | _|   \_/ |  //|\\
        |___||_|       /   \\\/\\

2. Required Software and Services

• .NET 8 SDK - Download here
• Azure Account - For Azure SQL Database and Azure OpenAI (optional for local development)
• SQL Server - Local instance or Azure SQL Database

Detailed Setup Guide

1. Clone the Repository

git clone https://github.com/your-repo/FinBin.git
cd FinBin

2. Configure Local Development

Option A: Local Database

• Update appsettings.Development.json:

"ConnectionStrings": {
  "AZURE_SQL_CONNECTIONSTRING": "Server=localhost;Database=FinBinDB;Trusted_Connection=True;MultipleActiveResultSets=true;TrustServerCertificate=True"
}

Option B: Azure SQL Database

• Update appsettings.json with your Azure SQL Database connection string:

"ConnectionStrings": {
  "AZURE_SQL_CONNECTIONSTRING": "Server=tcp:your-server.database.windows.net,1433;Initial Catalog=FinBinDB;Persist Security Info=False;User ID=your-username;Password=your-password;MultipleActiveResultSets=False;Encrypt=True;TrustServerCertificate=False;Connection Timeout=30;"
}

3. Configure Azure OpenAI & Semantic Kernel

• Update appsettings.json with your Azure OpenAI settings:

"AzureOpenAI": {
  "Endpoint": "https://your-openai-instance.cognitiveservices.azure.com/",
  "ApiKey": "your-api-key",
  "DeploymentName": "gpt-4"
}

4. Run Database Migrations

dotnet ef database update

5. Run the API

dotnet run

The API will be available at http://localhost:5263/swagger

Authentication

For development, we're using local authentication. In production, we'll implement secure authentication flows.

Reference: Secure Authentication Flows

Cloud Infrastructure

Azure SQL Database

FinBin uses Azure SQL Database for storing user data, financial records, and budget goals. The database is managed via Entity Framework Core, allowing seamless schema migrations and scalability.

Microsoft Semantic Kernel (AI Processing)

FinBin integrates Microsoft Semantic Kernel to analyze financial data and provide personalized AI-driven savings recommendations. Azure OpenAI models, such as GPT-4, process financial insights dynamically.

API Endpoints

User Management

• POST /api/users – Create a new user
• GET /api/users/{id} – Get user details

Financial Summary

• GET /api/financial-summary/{userId} – Retrieve financial summary
• PUT /api/financial-summary/{userId} – Update financial summary

Buckets (Savings Goals)

• POST /api/buckets/{userId} – Create a new savings goal
• GET /api/buckets/{userId} – List all buckets for a user
• PUT /api/buckets/{userId}/{bucketId} – Update a savings goal
• DELETE /api/buckets/{userId}/{bucketId} – Delete a savings goal

AI-Powered Budget Analysis

• GET /api/buckets/analyze/{userId} – AI analysis on user's financial health
• POST /api/buckets/adjust/{userId}/{bucketId} – AI-driven savings recommendations

AI-Driven Budget Analysis

The AI agent fetches a user's financial data and provides smart recommendations based on:

• Savings Timeline – How long will it take to reach a goal?
• Spending Adjustments – Suggested expense cuts to save more
• Probability Score (0-1) – Likelihood of achieving the goal in time

Example AI Response:

{
  "bucket": "Vacation Fund",
  "targetAmount": 5000,
  "currentSavedAmount": 200,
  "suggestedMonthlySavings": 400,
  "probabilityScore": 0.75,
  "recommendations": "Reduce subscriptions by $30 and allocate it to savings."
}

Troubleshooting

Common Issues

1. Database Connection Errors

   • Verify your connection string is correct
   • Ensure SQL Server is running
   • Check firewall settings for Azure SQL

2. Azure OpenAI Integration

   • Verify your API key and endpoint
   • Check deployment name matches your Azure OpenAI setup

3. Entity Framework Errors

   • Run dotnet ef migrations add InitialCreate if database doesn't exist

Future Enhancements

• AI chatbot for personalized financial coaching
• Auto-transfer savings recommendations
• Mobile app integration

Contributing

1. Fork the repository
2. Create a feature branch
3. Submit a pull request

License

MIT License.