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PYTHON Project
Credit Card Fraud Detection System Final Year Project Source Code
Get runnable Credit Card Fraud Detection System Final Year Project Source Code with database files, project setup instructions, live demo options and installation support. This project resource helps students understand the implementation, modules, workflow and technical architecture of a complete PYTHON project.
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Project's Overview
Credit Card Fraud Detection System is a Python Flask based final year project developed for detecting suspicious and fraudulent credit card transactions using rule-based risk scoring. This major project allows users to register, log in, reset passwords through security question and system-generated OTP, manage profiles, add transactions manually, upload transactions through CSV, validate transaction rows, check fraud for single or bulk transactions, view risk score, view reason tags, search/filter/sort records, download PDF or CSV reports, manage optional card profiles, view notifications, export user data, and clear personal transaction data. The system classifies each transaction as Safe, Suspicious, or Fraud based on risk factors such as unusual amount, odd transaction time, high-risk merchant, daily limit exceeded, and rapid transaction frequency. This Credit Card Fraud Detection source code is suitable for students who need a final year project, major project, minor project, source code, and project report based on Flask, SQLite, transaction analysis, and fraud detection logic.
Login Credentials
Demo User Account after seeding
- Username:
demo - Email:
[email protected] - Password:
Demo123
Seed data creates:
- 1 demo user
- 8 transactions
- 2 card profiles
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Dashboard Features in this Final Year Project
- Professional sidebar layout
- Transaction summary cards
- Total transaction count
- Checked transaction count
- Safe transaction count
- Fraud transaction count
- Suspicious transaction count
- Recent checked transactions
- Latest fraud status display
- Notification history
- Quick action links
- Upload transaction shortcut
- Add manual transaction shortcut
- Report download shortcut
Transaction Management Features in this Final Year Project
- Manual transaction entry
- CSV transaction upload
- Row-level CSV validation
- Invalid row error report
- Pending transaction status
- Fraud checked transaction status
- Single transaction fraud check
- Bulk transaction fraud check
- Check all pending transactions
- Transaction details page
- Transaction delete with confirmation
- Batch delete support
- Per-user transaction privacy
- User can only view own transactions
- Export and clear data controls
Fraud Detection Logic in this Final Year Project
This project uses a rule-based fraud scoring system. It does not require real banking datasets or external banking APIs.
Risk Factors
| Risk Factor | Score |
|---|---|
| Unusual amount | +30 |
| Late-night transaction | +20 |
| High-risk merchant | +25 |
| Daily limit exceeded | +40 |
| Rapid frequency | +15 |
Final Classification Rules
| Risk Score | Status |
|---|---|
| 0 – 30 | Safe |
| 31 – 60 | Suspicious |
| 61+ | Fraud |
Example
Amount high → +30
Late night → +20
Crypto merchant → +25
-------------------------
Total Risk Score → 75
Final Status → Fraud
Fraud Result Details Features in this Final Year Project
For every checked transaction, the user can view:
- Full transaction information
- Transaction amount
- Date and time
- Merchant name
- Transaction type
- Location
- Card profile
- Final fraud status
- Risk score
- Reason tags
- Check date and time
- Fraud explanation
- Human-readable risk reason
Example reason labels:
- Unusual transaction amount
- Transaction at odd hours
- High-risk merchant detected
- Daily limit exceeded
- Rapid transaction frequency detected
- Unusual location pattern
Internal Risk Analysis Modules in this Final Year Project
1. Amount Analysis
- Compares transaction amount with usual spending range
- Checks daily card limit if card profile is available
- Flags unusually high transactions
- Adds higher risk when amount crosses defined limit
2. Time-Based Pattern Check
- Detects late-night transactions
- Flags transactions between 12 AM and 5 AM
- Adds risk when transaction time looks unusual
3. Merchant Risk Pattern
- Detects high-risk merchant keywords
- Flags crypto platforms
- Flags gift card or voucher merchants
- Flags unknown or rarely used merchants
4. Location Deviation
- Checks unusual transaction location if available
- Adds risk for unknown or sudden location change
- Useful for fraud simulation logic
5. Frequency and Velocity Check
- Detects multiple transactions in short time
- Flags repeated transactions within minutes
- Adds risk for rapid retries or transaction bursts
Card Profile Features in this Final Year Project
- Add card nickname
- Add daily limit
- Add usual spending range
- Assign card profile to transaction
- Use card profile for risk calculation
- Detect daily limit exceeded
- Detect spending beyond normal range
- Improve transaction risk explanation
- Make project more complete without external banking integration
Report Features in this Final Year Project
Users can download reports in:
- PDF format
- CSV format
Report types:
- All transactions report
- Fraudulent transactions report
- Suspicious transactions report
- Custom date range report
- User transaction summary report
Report includes:
- User details
- Summary counts
- Transaction list
- Fraud status
- Risk score
- Reason tags
- Generated date and time
Notification Features in this Final Year Project
- In-app notification system
- Bulk check completed notification
- Fraud transaction alert
- Suspicious transaction alert
- Daily fraud summary message
- Dashboard notification display
- Notification history
Example notifications:
- 3 transactions marked Fraud today
- Bulk check completed
- CSV upload completed with 2 invalid rows
- Transaction report downloaded successfully
Help and Guidelines Features in this Final Year Project
- CSV format sample
- Upload instructions
- Fraud check steps
- Report download steps
- Common upload errors
- Common validation errors
- Transaction field explanation
- Safe/Suspicious/Fraud meaning
- User tips for better transaction tracking
Sample CSV file:
sample_data/sample_transactions.csv
Data Privacy Features in this Final Year Project
- Per-user data visibility
- User can view only own transactions
- User can delete own transactions
- User can delete uploaded batch records
- User can export data
- User can clear all personal data
- No external banking API required
- Fully local project execution
- Safe for academic demonstration
- User registration in this final year project
- User login
- User logout
- Forgot password using security question
- Forgot password using system-generated OTP
- Profile management
- Update full name
- Update email
- Update mobile number
- Change password with old password confirmation
- Dashboard with sidebar layout
- View total transactions uploaded/added
- View total checked transactions
- View fraud flag count
- View safe transaction count
- View recent activity
- Add transaction manually
- Enter transaction amount
- Enter transaction date
- Enter transaction time
- Enter merchant/shop name
- Select transaction type: online/offline
- Add location
- Add card type
- Add notes
- Validate transaction amount
- Validate transaction date and time
- Save transaction as pending check
- Upload transactions using CSV
- Validate CSV file format
- Validate required columns
- Skip invalid rows
- Generate row-level error report
- Store valid uploaded transactions
- Check single transaction
- Check multiple selected transactions
- Check all pending transactions
- View Safe/Suspicious/Fraud status
- View risk score
- View reason labels
- View full fraud result details
- Search transactions by merchant name
- Search transactions by transaction ID
- Filter transactions by status
- Filter transactions by date range
- Filter transactions by amount range
- Sort transactions by latest first
- Sort transactions by highest amount
- Sort transactions by risk score
- Download reports in PDF
- Download reports in CSV
- Download all transaction reports
- Download suspicious/fraud-only reports
- Download custom date-range reports
- Add optional card profiles
- Assign transaction to card profile
- View in-app notifications
- Delete own transactions
- Delete uploaded batch records
- Export user data
- Clear own data
- Access help and guidelines page
Machine Learning Note in this Final Year Project
This version does not require a real machine learning model.
The project intentionally uses a rule-based fraud detection logic because:
- Real fraud ML requires large private banking datasets
- Banking datasets are highly sensitive
- Rule-based fraud screening is still used in real-world systems
- It is easier to explain in viva
- It works fully offline
- It is safe for academic projects
ML-ready explanation:
The risk score can later be used as a feature set for Logistic Regression, Random Forest, or other machine learning models.
Other Features in this Final Year Project
- Complete Credit Card Fraud Detection source code
- Suitable for final year project, major project, and minor project
- Project report content can be prepared from included modules
- Flask web application
- SQLite database
- User authentication
- Forgot password module
- Security question verification
- OTP generated inside system
- Dashboard with sidebar layout
- Manual transaction entry
- CSV transaction upload
- Row-level CSV validation
- Fraud risk scoring
- Safe/Suspicious/Fraud classification
- Result details page
- Search/filter/sort transactions
- PDF report generation
- CSV report generation
- Optional card profiles
- In-app notifications
- Data privacy controls
- Help page
- Sample transaction CSV
- Demo seed data
- Useful for viva, source code review, project report writing, and project demonstration
-
Open terminal in the project folder:
cd "credit-card-fraud-detection-python-web" -
Create virtual environment:
python -m venv venv -
Activate virtual environment on Windows:
venv\Scripts\activate -
Activate virtual environment on Linux/macOS:
source venv/bin/activate -
Install dependencies:
pip install -r requirements.txt -
Run the application:
python run.py -
Open application:
http://127.0.0.1:5000/ -
Optional: seed dummy data:
python scripts/seed.py -
Use sample CSV for testing:
sample_data/sample_transactions.csv
README Included
Setup Support Available
Demo User Account after seeding
- Username:
demo - Email:
[email protected] - Password:
Demo123
Seed data creates:
- 1 demo user
- 8 transactions
- 2 card profiles