CashLens - An UPI Budget Manager
Your Money in Focus
Created on 30th December 2025
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CashLens - An UPI Budget Manager
Your Money in Focus
The problem CashLens - An UPI Budget Manager solves
The Problem CashLens Solves
Managing personal finances is increasingly difficult. People receive multiple UPI and bank SMS alerts every week, yet most still lack clarity about where their money is going. Several issues contribute to this:
Information overload causes important transaction details to get lost in SMS notifications.
Manual expense tracking apps require users to enter data themselves, leading to low engagement and incomplete records.
Users do not receive real-time insights or guidance about spending patterns, overspending, or potential savings.
Impulse spending on food delivery, subscriptions, and online shopping often goes unnoticed.
Many financial apps store sensitive data on external servers, raising privacy concerns.
The combined effect is scattered financial information, poor visibility, unnoticed overspending, and no actionable help.
How CashLens Helps Users and Makes Financial Tasks Easier and Safer
CashLens turns raw SMS alerts into a fully automated, private, and intelligent personal finance system. It improves users’ financial awareness and control by offering the following:
Automatic Expense Tracking
CashLens parses UPI/bank SMS alerts locally on the device and extracts transaction details instantly, eliminating the need for manual entry.
Clear and Organized Financial Overview
A visual dashboard presents monthly spending, category distributions, weekly trends, and top merchants, giving users immediate clarity without searching through messages.
AI-Driven Financial Insights
CashLens acts as a financial coach by highlighting overspending, identifying unusual transactions, predicting budget overruns, and suggesting ways to optimize expenses.
Subscription and Recurring Payment Detection
Users can easily identify active, unused, or hidden subscriptions and understand their monthly and annual cost impact.
Safety Through Anomaly Detection
The system flags suspicious or unexpected transactions, helping users spot potential issues early.
Privacy-First Design
All processing occurs locally in the browser, and no financial data is uploaded or stored on external servers, ensuring complete user privacy.
Summary :
CashLens simplifies personal finance by automating expense tracking, organizing spending patterns, detecting anomalies, and providing meaningful AI insights—all while keeping data entirely private. It allows users to understand and manage their money easily, accurately, and safely.
Challenges we ran into
Challenges We Ran Into :
Building CashLens presented several technical and design challenges that required careful problem-solving:
Reliable SMS Parsing and Transaction Extraction
Different banks and UPI apps use inconsistent SMS formats, which made it difficult to extract amounts, merchants, and transaction types accurately.
Solution:
We created a pattern-matching system with multiple fallback rules and tested it against a diverse mock SMS dataset. Iterative refinement improved accuracy significantly.
Categorizing Transactions with Limited Context
Many SMS messages lack explicit category information (e.g., whether a purchase is “Food” or “Shopping”).
Solution:
We implemented keyword-based classification combined with a lightweight NLP approach. Merchant names and transaction descriptions were mapped to known spending categories, improving classification consistency.
Ensuring Real-Time Performance in a Local-Only Environment
Since all parsing and analytics run in the browser for privacy reasons, heavy processing caused occasional lag.
Solution:
We optimized data structures, reduced unnecessary re-renders, and offloaded heavier computation into background logic. This made the dashboard feel responsive even with hundreds of transactions.
Setting Up a Clean and Intuitive Dashboard
Displaying trends, budgets, anomalies, and categories in a meaningful way was challenging from a UX perspective.
Solution:
We simplified the visual hierarchy, used Recharts for clarity, and iterated on UI layouts until insights could be understood at a glance.
Testing Anomaly Detection
False positives and false negatives occurred early when flagging unusual transactions.
Solution:
We refined detection rules and added threshold-based logic, improving the reliability of alerts.
Handling Routing and Deployment Issues
During deployment, certain deep-linked pages broke due to client-side routing.
Solution:
We fixed this by configuring proper rewrite rules for Netlify/AWS Amplify, ensuring SPA routes work on refresh
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