Strategic Fintech Platform
Open Finance Security & Reverse Engineering
Security work performed under NDA. Technical approach shared; specific vulnerabilities and findings are confidential.
The Challenge
Building reliable credit scoring in Nigeria requires aggregating data from unconventional sources - bank statements, mobile money, and alternative data. The challenge was both technical (reverse engineering financial APIs) and security-focused (ensuring data integrity and privacy).
The Approach
Performed security audits on the platform's API infrastructure, identified vulnerabilities in third-party integrations, and reverse-engineered financial data APIs for credit scoring model inputs. Also contributed to the machine learning pipeline for credit score generation.
System Architecture
A security engineering layer within a larger fintech platform. My work centered on three areas: (1) API security auditing - penetration testing the platform's endpoints, identifying authentication bypass vulnerabilities, and establishing security audit processes. (2) Financial API reverse engineering - analyzing undocumented Nigerian financial data provider APIs (bank statement aggregators, mobile money platforms) to understand their actual response formats, error patterns, and data structures, then building reliable integration adapters. (3) ML pipeline contribution - normalizing reverse-engineered financial data into clean feature vectors for the credit scoring model, working alongside the data science team to validate input data quality.
Built For
Nigerians without traditional credit history who need access to loans and financial services - gig workers, small traders, and young professionals whose financial footprint exists in mobile money and bank statements rather than FICO-style credit bureau records. The platform serves both end borrowers and lending institutions that need reliable alternative credit scoring.
Design Decisions
Why reverse engineering for credit scoring?
Many Nigerian financial data providers have poorly documented APIs. Reverse engineering their actual response formats, error patterns, and data structures was the only way to build reliable integrations - documentation was either missing or wrong.
Why Python for the security tooling?
Python's ecosystem for security (requests, Scapy, Burp Suite extensions) and data analysis (pandas, sklearn) made it the natural choice for a role that spans both domains.
The Team
Security Engineer (me)
API security auditing, penetration testing, reverse engineering financial APIs, ML pipeline contributions
Data Scientist
Credit scoring model design, feature engineering, model validation
Backend Engineers (3)
Core platform development, microservices, data ingestion pipelines
Compliance Officer
CBN regulatory requirements, data privacy policies, audit trail design
Tech Stack
Outcomes & Impact
- Identified and patched critical API vulnerabilities before production launch
- Reverse-engineered 3+ financial data provider APIs for reliable integration
- Collaborated with data science team to validate ML model inputs from reverse-engineered API data
- Contributed to credit scoring ML pipeline processing thousands of applications
- Established security audit processes adopted by the engineering team
- Worked cross-functionally with compliance to ensure reverse-engineered integrations met CBN data handling standards
💬 Behind the Scenes
“Credit scoring in emerging markets is a completely different beast from the FICO model. When most of your users don't have traditional credit history, you learn to find signal in unexpected places.”