Inspiration
Two people can earn the same salary and end up with completely different financial futures. The difference is often not income, but behavior. While most finance apps show transactions and spending summaries, they fail to explain how everyday habits impact long-term wealth. We wanted to build a platform that helps users understand the financial future their current behavior is creating. This led to the idea of FinGraphX—a Graph-AI powered financial intelligence platform that transforms transaction data into actionable wealth insights.
What it does
FinGraphX uses Neo4j Knowledge Graphs and AI to connect users, transactions, merchants, and spending categories. It identifies hidden budget leaks, spending communities, and recurring financial behaviors that traditional budgeting apps miss. The platform generates a Financial DNA profile, calculates a Wealth Score, and creates a Financial Twin that simulates how small spending changes today can impact future wealth. Users can also explore financial goals through a Dream Goal Simulator that estimates how quickly they can achieve milestones such as travel, buying a car, or financial freedom.
How we built it
We built FinGraphX using Neo4j as the graph database, Streamlit for the user interface, Python and Pandas for data processing, Plotly for visualizations, and NetworkX for graph analysis. Transaction data is converted into a knowledge graph connecting users, transactions, merchants, and categories. We then apply graph-based analysis and financial simulations to generate personalized insights, wealth projections, and recommendations.
Challenges we ran into
One of the biggest challenges was moving beyond a traditional budgeting dashboard and creating a solution where graph technology provided real value. We had to design a graph structure that could meaningfully represent spending behavior and convert complex analytics into simple, actionable recommendations.
Accomplishments that we're proud of
Built an end-to-end Graph-AI finance platform. Developed Financial DNA and Financial Twin concepts. Demonstrated how graph technology can uncover hidden spending behaviors. Created a user-friendly experience from complex financial data
What we learned
Knowledge graphs reveal patterns that traditional databases often miss. Relationships, not isolated transactions drive financial behavior. Graph analytics can make personal finance more personalized and actionable. Small spending changes can have significant long-term wealth impacts.
What's next for FinGraphX
AI-powered financial coaching. Fraud and anomaly detection.
Log in or sign up for Devpost to join the conversation.