Building a fintech app with Python? Let us review it.
Expert code review for fintech apps built with Python. We fix Python-specific security gaps, optimize performance, and handle deployment. From $19.
Common Python issues we find
Real problems from Python codebases we've reviewed.
Django debug mode in production
DEBUG=True left enabled in production, exposing stack traces, database queries, and configuration to attackers.
Missing CSRF protection
CSRF middleware disabled or bypassed for convenience, allowing cross-site request forgery attacks.
Insecure deserialization
Using pickle or yaml.load with untrusted data, enabling remote code execution.
Slow database queries
ORM queries that generate inefficient SQL, N+1 query patterns, and missing database indexes.
Fintech App challenges to solve
Key fintech app concerns that AI-generated code often misses.
Data encryption and storage
Financial data must be encrypted at rest and in transit. Account numbers, transaction histories, and personally identifiable financial information need field-level encryption, not just HTTPS. AI tools store financial data in plain text in the database, which is a compliance violation waiting to happen.
Transaction integrity
Financial transactions must be atomic — money debited from one account must be credited to another, with no in-between state where it disappears. AI-generated code doesn't use database transactions, meaning a server crash mid-operation can leave accounts in an inconsistent state.
Audit trail and compliance
Every financial action needs an immutable log — who initiated it, when, what changed, and what the balances were before and after. Regulators require this. AI tools don't generate audit logging, and retrofitting it into an existing codebase is tedious but essential.
Third-party financial API integration
Connecting to Plaid, Stripe, or banking APIs requires handling OAuth flows, webhook verification, idempotency keys, and retry logic for failed calls. AI tools generate the initial API call but miss the error handling and reliability patterns these services require.
What we check
Key areas we review for Python fintech app projects.
Data encryption — field-level encryption for sensitive financial data
Transaction integrity — atomic operations, no partial state changes
Audit logging — immutable records of every financial action
Authentication — MFA, session management, account lockout policies
Not sure if your app passes? Our code audit ($19) checks all of these and more.
Start with a self-serve audit
Get a professional review of your Python fintech app project at a fixed price.
External Security Scan
Black-box review of your public-facing app. No code access needed.
- OWASP Top 10 vulnerability check
- SSL/TLS configuration analysis
- Security header assessment
- Expert review within 24h
Code Audit
In-depth review of your source code for security, quality, and best practices.
- Security vulnerability analysis
- Code quality review
- Dependency audit
- Architecture review
- Expert + AI code analysis
Complete Bundle
Both scans in one package with cross-referenced findings.
- Everything in both products
- Cross-referenced findings
- Unified action plan
100% credited toward any paid service. Start with an audit, then let us fix what we find.
How it works
Tell us about your app
Share your project details and what you need help with.
Expert + AI audit
A human expert assisted by AI reviews your code within 24 hours.
Launch with confidence
We fix what needs fixing and stick around to help.
Frequently asked questions
Can you review a fintech app built with Python?
Yes. We regularly audit Python fintech app projects and understand the specific patterns and pitfalls of this combination. Our review covers security, performance, and deployment readiness.
What issues do you find in Python fintech apps?
Common issues include django debug mode in production and missing csrf protection on the Python side, combined with fintech app-specific concerns like data encryption and storage and transaction integrity. We check for all of these and more.
How do I make my Python fintech app production-ready?
Start with our code audit ($19) to get a prioritized list of issues. For Python fintech app projects, the typical path is: fix security gaps, address fintech app-specific requirements, optimize Python performance, then configure deployment. We provide a fixed quote after the audit.
How long does it take to audit a Python fintech app?
Our code audit delivers a full report within 24 hours. For Python fintech app projects, we check security, architecture, performance, and deployment readiness across all Python-specific patterns. Fixes are scoped separately with a fixed quote.
Related resources
Python by Use Case
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