Python testing strategies for fintech

Testing financial software requires extra care-bugs can cost money and violate compliance. After building payment systems at Mercury and Heard, I've learned that testing strategies need to balance thoroughness with practicality.

The Challenge

Financial code must be:

• Accurate (no calculation errors)
• Secure (no vulnerabilities)
• Compliant (meets regulations)
• Reliable (handles edge cases)

Testing Pyramid

Unit Tests (70%) Test individual functions and classes in isolation.

Integration Tests (20%) Test how components work together.

End-to-End Tests (10%) Test complete user flows.

Example: Testing Payment Processing

import pytest
from decimal import Decimal
from payment_processor import process_payment

def test_process_payment_success():
    result = process_payment(
        amount=Decimal('100.50'),
        currency='USD',
        recipient_id='user123'
    )
    assert result.success is True
    assert result.transaction_id is not None

def test_process_payment_insufficient_funds():
    with pytest.raises(InsufficientFundsError):
        process_payment(
            amount=Decimal('1000.00'),
            currency='USD',
            recipient_id='user123'
        )

def test_process_payment_negative_amount():
    with pytest.raises(ValueError):
        process_payment(
            amount=Decimal('-100.00'),
            currency='USD',
            recipient_id='user123'
        )

Best Practices

1. Use Decimal for money (never float)
2. Test edge cases (zero, negative, very large amounts)
3. Mock external APIs (banks, payment processors)
4. Test idempotency (retries don't cause issues)
5. Test concurrency (race conditions)

"In fintech, a bug isn't just a bug-it's a potential financial loss."

Tools

• pytest for testing framework
• pytest-asyncio for async tests
• pytest-mock for mocking
• hypothesis for property-based testing
• coverage.py for code coverage