Pocket Sense
-Financial freedom starts with knowledge personalized insights and actionable data.
Created on 11th September 2024
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Pocket Sense
-Financial freedom starts with knowledge personalized insights and actionable data.
The problem Pocket Sense solves
Pocket Sense is a comprehensive financial literacy platform designed to help users make informed financial decisions.
Here are some key ways it solves existing problems:
• Personalized Investment Suggestions: Users can able take a quiz to get a tailored investment plans based on their financial goals, risk tolerance, and preferred investment timeframe. This is achieved using machine learning models like Decision Trees( initial plan), Random Forests, and K-Means Clustering.
• Expense Tracking with Dynamic Budgeting: The platform logs daily expenses, categorizes them, and generates budget templates. It uses Recurrent Neural Networks (RNNs) and Long Short-Term Memory (LSTM) models to predict future expenses and provide real-time budget adjustments.
• Investment Portfolio Management: It consolidates user investments and forecasts future returns using Bayesian Networks and Monte Carlo simulations, helping users assess risk and make data-driven decisions.
• Group Expense Manager: Users can able to log group expenses, and the system splits the bill among participants as required , tracks contributions, and calculates outstanding amounts using Gradient Boosting Machines (GBM) and Rule-Based AI Systems
• EMI Calculator: Provides a simple mathematical formula for calculating loan repayments, helping users understand their financial commitments from expense data from Expense Tracker before taking a loan.
• Real-Time Chat Room: Enables users to engage in discussions on market trends, share investment strategies, and get advice on portfolio management, fostering community interaction.
Challenges we ran into
While building Pocket Sense, our team encountered several significant challenges:
- Integration of Multiple Machine Learning Models
Challenge: Ensuring seamless integration of various ML models (Decision Trees, Random Forests, K-Means Clustering, RNNs, LSTM, Bayesian Networks, Monte Carlo simulations) to provide cohesive financial insights.
Solution:
- Implemented a modular approach, developing and testing each model independently before integration.
- Established standardized data formats across models.
- Conducted regular integration testing to catch and resolve issues early.
- Real-Time Data Processing
Challenge: Handling real-time data for dynamic budgeting and investment portfolio management.
Solution:
- Utilized Node.js and Express for efficient backend processing.
- Implemented MongoDB for fast data storage and retrieval.
- Used WebSockets for real-time updates in the chat room.
- Scalability and Performance
Challenge: Ensuring platform scalability without compromising performance.
Solution:
- Implemented load balancing to distribute traffic.
- Used caching mechanisms to reduce database load.
- Optimized database queries for improved response times.
- User Authentication and Security
Challenge: Ensuring secure user authentication and data protection.
Solution:
- Implemented JWT-based authentication.
- Encrypted sensitive data in transit and at rest.
- Conducted regular security audits.
- User Experience and UI/UX
Challenge: Creating an intuitive interface for complex financial tasks.
Solution:
- Followed user-centered design principles.
- Implemented feedback loops for continuous UI/UX improvement.
- Ensured responsive design across various devices.