Credit Risk Officer at Ellington Bank

The Role

• As our Credit Risk Officer, you will be the architect of our digital lending safety net. You will not just manage risk; you will build the systems that automate it. Your primary focus will be overseeing algorithmic credit scoring, integrating alternative data, and ensuring our loan portfolio remains healthy as we scale aggressively.

Key Responsibilities

• Algorithmic Oversight: Review and tune automated credit scoring models to ensure high-speed, accurate decisions.
• Alternative Data Integration: Analyse traditional and non-traditional data points (BVN, telco data, utility patterns) via APIs like Mono or Okra.
• Product Collaboration: Partner with the Product and Engineering teams to define risk parameters (interest rates, repayment triggers) and translate policies into code logic.
• Portfolio Monitoring: Use real-time dashboards to track Portfolio at Risk (PAR) and configure automated early warning triggers.
• Regulatory Compliance: Ensure all digital lending practices comply with CBN standards and Nigeria Data Protection Act (NDPA) guidelines.
• Risk Mitigation: Oversee Global Standing Instruction (GSI) protocols and manage Grey Zone applications that require manual intervention.

Performance Targets

You will be evaluated on your ability to maintain a balanced approach to risk and growth:

• PAR 30: Maintain at less than 4%.
• NPL Cap: Maintain below 4%.
• Automation: Achieve 95% Straight Through Processing (STP) for instant decisions.
• Cost of Risk: Keep below 4%.

Requirements

• Proven experience in credit assessment and data analysis within a digital banking or Fintech environment.
• Strong understanding of KYC, fraud validation, and identity verification filters.
• Knowledge of Nigerian regulatory frameworks (CBN, NDPR, NDIC
• Ability to conduct stress testing against macroeconomic shocks.
• 5+ years of proven track record in risk management, specifically within digital lending or microfinance.
• Minimum of a B.Sc. degree; candidates with a background in data-driven decision-making are preferred.