HackStack

Project Info

FinPilot AI

Devpost

Inspiration

Managing personal finances typically involves switching between multiple applications, one for banking, another for investments, a third for market news, and yet another for financial questions. Each tool solves a specific problem, but none provides a complete view of a user's financial life. We wanted to build a single platform that combines expenses, investments, market insights, and financial guidance in one place. Instead of just displaying transactions and charts, FinPilot helps users understand their finances through meaningful insights and contextual information. What It Does FinPilot combines everyday financial management with investment tracking and intelligent analysis into a single platform. Expense Management Automatically imports bank transaction SMS messages on Android Detects income and expenses Categorizes transactions Tracks spending over time Generates summaries and spending insights Identifies recurring payments and unusual spending patterns Investment Tracking Monitor stock portfolios Create and manage watchlists Research companies and market trends View portfolio performance and allocation Track sector diversification Financial Assistant FinPilot includes an integrated financial assistant that helps users understand their spending patterns, analyze portfolio performance, learn investment concepts, answer questions using their own financial data, and explore budgeting strategies. Instead of providing generic responses, it uses the user's financial information to deliver relevant and personalized guidance. Personalized Notifications Rather than showing every market headline, FinPilot highlights news and updates relevant to the user's portfolio and watchlists. Intelligent Report Generation FinPilot automatically generates comprehensive financial reports that analyze user data and present actionable insights. The system processes transaction history, portfolio data, and spending patterns to create professional reports with charts and visualizations. Users can export these reports as PDF files for sharing with financial advisors or for personal record keeping. Goal Tracking Users can set financial goals such as saving for a car, building an emergency fund, or growing an investment portfolio. FinPilot tracks progress against these goals, estimates completion timelines based on current savings rates, and provides recommendations to accelerate progress. Smart Alerts FinPilot monitors financial activity and generates alerts for situations that may require attention, including budget exceedances, unusual spending patterns, portfolio concentration risks, and important market events relevant to user holdings. Why We Used Codex and GPT We incorporated OpenAI's Codex and GPT models at different stages of development to accelerate building while enabling intelligent user-facing features. Codex for Development Acceleration Codex acted as an engineering partner throughout the development lifecycle. It helped us design the overall system architecture, plan a scalable project structure, and define API flows. In the frontend, Codex accelerated development of React components, responsive layouts, dashboards, and interactive forms. For the backend, it assisted in creating FastAPI endpoints, handling file uploads, organizing API routes, and structuring the application into modular components. When we encountered issues, Codex helped identify root causes and suggest improvements. It also helped refactor repetitive code into reusable components, making the application easier to maintain. By reducing time spent on repetitive development tasks, Codex allowed us to focus on building better features and improving the overall user experience. GPT for Intelligence GPT powers the core intelligence features of FinPilot. When users upload transaction history, GPT analyzes spending patterns, categorizes expenses, identifies recurring payments, and generates personalized financial insights. For stock research, GPT explains companies in simple language by generating business summaries and highlighting strengths and risks. The Financial Assistant allows users to ask questions about their finances and receive context-aware responses based on their financial information instead of generic answers. GPT also generates personalized financial reports that summarize spending behavior, highlight important insights, and recommend practical actions users can take to improve their financial well-being. Together, Codex helped us build FinPilot efficiently, and GPT made it intelligent by transforming financial data into meaningful insights and natural conversations. How We Built It Frontend React with Vite for the web application React Native CLI for the Android mobile application Backend Express.js for the API server TypeScript for type safety OpenAI APIs for intelligent features Alpha Vantage APIs for market data Database Supabase as the backend-as-a-service platform PostgreSQL for data storage Row-Level Security (RLS) for data protection Authentication via Supabase Auth Cloud Storage for file handling Both web and Android applications share the same backend and database, allowing data captured on one device to instantly appear across the platform. The database was designed as the single source of truth, with separate models for transactions, investments, watchlists, alerts, reports, and goals. Technical Highlights Automatic SMS Transaction Parsing Instead of relying on sender IDs, which vary significantly between banks, we analyze the content of each SMS to identify financial transactions. The parser can detect debit and credit transactions, extract amounts, identify merchants, ignore OTP and promotional messages, distinguish balances from transaction amounts, and prevent duplicate uploads. This approach supports multiple banks without maintaining bank-specific rules. Secure Multi-User Architecture Financial data is protected using PostgreSQL Row-Level Security (RLS), ensuring users can only access their own records. Authentication and authorization are integrated throughout the application to provide secure access across web and mobile platforms. Efficient Request Processing Most requests are handled through database queries and application logic before invoking language models. This keeps responses fast, reduces unnecessary API usage, and lowers operational costs while reserving advanced processing for tasks that genuinely benefit from it. Data Visualization FinPilot uses interactive charts and visualizations to help users understand their financial data. Spending distributions, monthly trends, and portfolio allocations are displayed through responsive charts that make complex data easier to interpret. Challenges We Ran Into Building FinPilot required solving problems across mobile development, backend architecture, databases, and financial data processing. Key challenges included: Supporting multiple bank SMS formats Preventing duplicate transaction imports Designing a scalable financial database Implementing secure multi-user access with Row-Level Security Synchronizing data between mobile and web applications Balancing advanced language features with performance and cost Managing collaboration and Git workflows with multiple contributors

Analysis

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Metric

Figures cover GitHub contributors during the hackathon window. A co-authored commit counts in full for each author, so per-member totals add up to more than the whole-team figures.

Technology

Found in codeClaimed only
  • CSSIn code
  • ExpressIn code
  • HTMLIn code
  • JavaScriptIn code
  • OpenAIIn code
  • ReactIn code
  • SupabaseIn code
  • Tailwind CSSIn code
  • TypeScriptIn code
  • Node.jsClaimed
  • PostgreSQLClaimed
  • VercelClaimed

9 of 12 appear in the indexed code. 3 claimed on Devpost could not be matched to code, which may simply mean the tool leaves no trace in the repository.

AI coding agents

  • GitHub CopilotCommits

Detected from committed agent config files and commit authorship. Absence of a signal is not proof an agent was unused.

Codebase size

Source size

401 KB

Source files

38

Counts recognized source files only; vendored directories, binaries and lockfiles are excluded, so this is smaller than the repository on disk.

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