FinTrack AI is a personal finance analytics dashboard I built to explore how structured financial data can be transformed into decision-ready insights using KPI modeling, behavioral profiling, and goal feasibility estimation.
This project reflects how I approach data analysis problems:
raw inputs → meaningful metrics → interpretable dashboards → decision-support insights
Portfolio Project — Built for Data Analyst role preparation.
https://fintrack-ai.onrender.com
This isn't a chart-on-top-of-a-spreadsheet project.
The backend calculates a full set of derived financial indicators from raw income and expense inputs — savings rate, expense volatility, benchmark score, behavior profile, momentum score, survival months, and goal feasibility timelines. These are metrics that real personal finance tools use, implemented from scratch in Python.
The analytics pipeline looks like this:
income + expense inputs → SQL aggregation → KPI engine → behavioral scoring → decision-ready dashboard
The analytics workflow inside FinTrack AI follows a layered decision-support pipeline:
User Input Layer
→ Flask API Layer
→ SQL Aggregation Layer
→ KPI Computation Engine
→ Behavioral Scoring Engine
→ Goal Feasibility Engine
→ Dashboard Visualization Layer
Each layer transforms raw financial inputs into interpretable behavioral signals that support better personal finance decisions.
Shows quick-access navigation panels, KPI summary cards, and AI-generated daily financial insight highlights.
Displays savings rate gauge, income vs expense comparison, and top spending category detection.
Interactive donut chart and ranked category spending comparison using Chart.js visualization.
Predicts timeline completion using surplus capacity and evaluates whether targets are realistically achievable.
Rule-based personalized recommendations generated from behavioral indicators and KPI thresholds.
Summarizes income allocation, savings capacity, and disposable balance at a glance.
Once you enter your income and expense data, the system automatically generates:
Core Metrics
- Monthly income (primary + additional sources)
- Total monthly expenses across 11 categories
- Net disposable income and savings capacity
- Savings rate as a percentage of total income
Behavioral Indicators
- Behavior profile — Saver, Balanced Planner, Growth Builder, or Risk Spender
- Benchmark score out of 100 based on standard financial health thresholds
- Momentum score that weighs savings rate against expense ratio
- Expense volatility — how evenly distributed spending is across categories
- Income stability classification — stable, moderate, or variable
Goal Intelligence
- Goal feasibility estimation based on current monthly surplus
- Completion timeline in months
- Goal probability score (0–1 scale)
- Active vs completed goal tracking
Risk Indicators
- Overspending flag when total expenses exceed income
- Survival months — how long current savings would last if income stopped
- Emergency fund readiness assessment
The core of this project is the /api/analytics endpoint in app.py. It does more than aggregate — it interprets.
Savings rate calculation:
Savings Rate = (Net Balance / Total Income) × 100
Benchmark scoring — 25 points each for:
- Savings rate ≥ 20%
- Rent-to-income ratio ≤ 30%
- Entertainment spending ≤ 10% of income
- Emergency allocation ≥ 5% of income
Behavior profiling based on savings rate thresholds:
- ≥ 30% → Saver
- ≥ 20% → Balanced Planner
- ≥ 10% → Growth Builder
- Below 10% → Risk Spender
Goal feasibility:
Months to goal = Remaining Target / Monthly Net Balance
Goal probability = Monthly Surplus / Required Monthly Contribution
Expense volatility
Uses standard deviation of category spending normalized by average — higher volatility means uneven, harder-to-predict spending behavior.
Onboarding Flow
- OTP-based email verification via Brevo API
- Income profile setup — primary and additional income sources, dependant count
- 11-category expense profile entry
Dashboard
- Live KPI cards with behavioral interpretation
- Income vs expense comparison
- Savings rate gauge
- Top expense category detection
- Goal snapshot (last 3 active goals)
Analytics Page
- Savings rate gauge visualization
- Income vs expense trend chart
- Expense category donut chart
- Spending ranking by category
- Goal feasibility and timeline panels
- Behavior profile and benchmark score display
Goals Tracker
- Create savings goals with target amounts and monthly contributions
- Active / completed goal filtering
- Feasibility estimation against current financial profile
Settings
- Update display name and gender
- Export all data as Excel (.xlsx) — user profile, income, expenses, goals as separate sheets
| Layer | Technology |
|---|---|
| Backend | Python 3, Flask |
| Database | SQLite |
| Frontend | HTML, CSS, JavaScript |
| Charts | Chart.js |
| Email / OTP | Brevo Transactional Email API |
| Data Export | pandas + openpyxl |
| Auth | Session-based with OTP verification |
| Deployment | Render |
Fintrack-AI/
├── app.py
├── recommendation.py
├── requirements.txt
├── LICENSE
├── SECURITY.md
templates/
├── index.html
├── about.html
├── features.html
├── contact.html
├── privacy.html
├── signup.html
├── login.html
├── dashboard.html
├── analytics.html
├── income.html
├── expense.html
├── goals.html
├── settings.html
Python 3.10+
A Brevo account (free tier works) for OTP emails
Get API key → https://brevo.com
Clone repo
git clone https://github.com/Kirito263V/Fintrack-AI.git
cd Fintrack-AI
Create virtual environment
python -m venv venv
Activate environment
Linux / macOS
source venv/bin/activate
Windows
venv\Scripts\activate
Install dependencies
pip install flask werkzeug pandas openpyxl requests
Set Brevo API key
export BREVO_API_KEY=your_brevo_api_key_here
Run app
python app.py
Visit:
Create Web Service on render.com
Start command
gunicorn app:app
Environment variable
BREVO_API_KEY = your_brevo_api_key
Deploy
SQLite database is created automatically on first run.
| Method | Endpoint | Description |
|---|---|---|
| GET | /api/dashboard | Full dashboard data |
| GET | /api/analytics | Complete analytics payload |
| GET | /api/goals | Goal list |
| POST | /api/goals | Create goal |
| PATCH | /api/goals/ | Update goal |
| DELETE | /api/goals/ | Delete goal |
| POST | /api/income | Save income |
| POST | /expense | Save expense |
| GET | /api/me | Current user profile |
| PATCH | /api/me | Update profile |
| GET | /api/export-excel | Download Excel export |
| POST | /send-otp | Send OTP |
| POST | /verify-otp | Verify OTP |
| POST | /login | Authenticate |
| POST | /logout | Clear session |
I wanted to build something that goes past basic aggregation.
Every personal finance app shows you a pie chart of your spending.
Very few tell you what that pattern means or whether your financial goals are actually on track.
The behavioral profiling and benchmark scoring logic in this project were inspired by real financial advisor classification frameworks translated into interpretable analytics rules.
The goal feasibility engine ensures savings targets align realistically with income-expense gaps automatically.
- Time-series spending trend tracking across multiple months
- Predictive savings forecasting using regression modeling
- PDF report export with full analytics summary
- PostgreSQL migration for multi-user production deployment
- Spending anomaly detection
Binaya Kumar Meher
I build analytics dashboards that go beyond displaying data — the goal is always to derive indicators that support real decisions.
GitHub
https://github.com/Kirito263V
LinkedIn
www.linkedin.com/in/binaya-kumar-da
MIT License