Spending Shield

Spending Shield Logo

Inspiration

Consumer debt is at an all-time high, and many individuals struggle to stay within their financial means. Traditional budgeting apps provide static insights but fail to prevent overspending in real time. We wanted to create Spending Shield, a machine-learning-powered tool that not only tracks spending but proactively helps both individuals and banks reduce consumer debt, improve financial habits, and enhance member retention.

By combining machine-learning-driven insights with behavioral nudges, we aim to empower people to make better financial decisions while giving banks a scalable way to support their customers.

What it does

Spending Shield analyzes real-time transaction data to:
✅ Detect risk patterns – Identifies when a user is overspending based on their income, budget, and past behavior.
✅ Send proactive alerts – Notifies users when they exceed their means, not after.
✅ Improve customer retention for banks – Helps financial institutions reduce loan defaults and increase engagement by providing personalized financial wellness tools.

How we built it

We used a combination of machine learning, financial analytics, and user behavior modeling to develop Spending Shield.

Tech Stack: MongoDB Atlas (database), Streamlit, Pandas, NumPy, Kaggle, matplotlib
ML Models: Trained on generated spending data to predict overspending risk and generate personalized fraud detection models.
API Integrations: Linked to Capital-One's API for real-time transaction monitoring.

Challenges we ran into

Vision –One of our biggest challenges in creating Spending Shield was the lack of a clear vision and goal from the start. As a team, we struggled with defining the app’s core purpose, which led to shifts in direction and inefficiencies in development.
Balancing an Accurate Database & Efficient Code – Ensuring Spending Shield sends and stores accurate data into our mongodb database was a prevalent issue we spent time overcoming.
Optimizing ML Predictions – Training our model to detect overspending patterns across different demographics and spending behaviors required lots of fine-tuning.

Accomplishments that we're proud of

🏆 Successfully implemented real-time ML-powered spending alerts.
🏆 Built an intuitive user dashboard that transforms financial insights into easy-to-follow recommendations.
🏆 Created a prototype that can be seamlessly integrated with banks to improve member retention and reduce loan delinquency.

What we learned

📌 ML-powered financial tools are most effective when they provide real-time insights rather than static reports.
📌 User psychology is just as important as data – people don’t just need budget numbers, they need encouragement and behavioral nudges.
📌 Financial institutions are actively seeking AI-driven solutions to improve customer retention and financial health.

What's next for Spending Shield

🔹 Expanding our ML/AI models – Improving our spending prediction accuracy by incorporating more behavioral and economic factors.
🔹 Bank Partnerships – Integrating Spending Shield with financial institutions to provide real-time financial wellness solutions to their customers.
🔹 Gamification & Rewards – Adding a financial wellness score, achievement system, and character classification to make saving money more engaging. 🔹 Cross-Platform Expansion – Developing a mobile app and browser extension for even more accessibility. 🔹 Generative-AI Chat Bot - Integrating a generative-ai chatbot within our application that provided real-time and personal feedback to questions about what the user is seeing.