• Application Deadline:
• Position: Senior Collections Risk Analyst



• Job Type Full Time , Remote



• Qualification BA/BSc/HND



• Experience 3 years



• Location Lagos




• Job Field Finance / Accounting / Audit 
  
  

About the role

• A highly analytical professional with deep expertise in Expected Credit Loss (ECL) modeling forecasting and collections risk analysis. This role is critical in shaping data-driven recovery strategies by analyzing delinquency trends, risk segmentation, and portfolio performance.


• The individual must have a strong understanding of how predictive models work, impact collections strategies, and how to interpret their outputs to optimize recovery efforts.


• The individual will be responsible for analyzing risk trends, evaluating collections effectiveness, and providing actionable insights to improve recoveries.


• This position requires hands-on experience with SQL, Python (for data analysis), and statistical modeling concepts, as well as a thorough understanding of how underwriting decisions and collections operations impact Expected Credit Loss and overall portfolio risk.

Requirements

ECL Modeling & Forecasting:

• Analyze and interpret ECL models and forecasts, providing insights into expected recoveries and risk exposure.


• Utilize historical delinquency and recovery data to assess the accuracy of ECL projections and recommend refinements.


• Perform vintage analysis and roll-rate modeling to understand credit deterioration and its impact on collections risk.


• Support stress testing efforts to evaluate portfolio performance under different collections strategies and economic conditions.


• Monitor and assess loss provisioning trends, ensuring alignment between collections strategies and expected recoveries.

Collections Performance Analytics & Risk Segmentation:

• Analyze cohort performance, delinquency trends, and borrower segmentation to optimize collections strategies.


• Evaluate the effectiveness of existing collections treatment paths, identifying areas for improvement.


•  Assess the impact of credit underwriting decisions on collections outcomes, ensuring alignment between risk assessment and recovery strategies.


• Support the design and execution of A/B testing for different collections approaches, using data to recommend optimal strategies.


• Monitor roll rates and transition matrices to detect early signs of delinquency risk and recommend intervention strategies.

Understanding of Predictive Models & Strategy:

• Interpret the outputs of propensity-to-pay models and predictive risk models, using insights to refine collections outreach.


• Work closely with data science teams to understand how machine learning models assess collections risk and borrower behavior.


• Leverage model-driven insights to enhance borrower segmentation, call center efficiency, and digital engagement strategies.


• Identify leading indicators of non-repayment, ensuring proactive collections intervention before delinquency worsens.


• Collaborate with strategy teams to refine contact strategies based on predictive insights, improving recovery rates.

Collaboration & Process Improvement:

• Work closely with finance, risk, and collections operations teams to ensure accurate forecasting and risk assessment.


• Provide data-driven recommendations to improve collections efficiency, reduce cost to collect, and enhance customer engagement.


• Develop automated reporting and dashboards for tracking collections KPIs, recovery rates, and delinquency trends.


• Support the Collections Analytics Manager in refining risk models and implementing strategy improvements based on data insights.


• Evaluate and recommend new data sources to improve collections risk analysis and forecasting accuracy.

Experience & Risk Management Expertise

• 3+ years of experience in collections analytics, credit risk, or a related data-driven role.


• Strong track record in forecasting delinquency trends and optimizing loss provisioning strategies.


• Experience working with ECL models, understanding their inputs, outputs, and business implications.


• Understanding of underwriting policies and how they influence collections risk and recovery strategies.


• Experience in A/B testing for collections strategy optimization.


• Strong ability to interpret predictive model outputs and apply insights to optimize collections operations.

Key Skills & Qualifications: Technical & Analytical Skills:

• Advanced proficiency in SQL and Python for data extraction, manipulation, and analysis.


• Strong expertise in Expected Credit Loss (ECL) modeling, loss forecasting, and provisioning calculations.


• Familiarity with statistical modeling, machine learning outputs, and predictive analytics in a credit risk or collections setting.


• Understanding of vintage analysis, roll-rate modeling, and transition matrices for delinquency risk assessment.


• Experience with Power BI, Tableau, or similar visualization tools to present collections insights effectively.


• Knowledge of IFRS 9 and other credit risk regulatory frameworks affecting ECL calculations.

Communication & Stakeholder Engagement:

• Strong ability to translate complex data findings into actionable recommendations for senior leadership.


• Experience working cross-functionally with finance, risk, and collections operations teams.


• Ability to present technical insights in a clear, non-technical manner to business stakeholders.


• Strong written and verbal communication skills to drive alignment on collections risk strategy.

Desired Traits:

• Highly Analytical: Strong problem-solving skills with the ability to break down complex data into actionable insights.


• Detail-Oriented: Ensures accuracy in reporting and forecasting to minimize risk exposure.


• Proactive: Continuously seeks ways to improve ECL forecasting, risk segmentation, and collections efficiency.


• Results-Driven: Focused on optimizing recovery rates and minimizing losses through data-driven strategy execution.


• Adaptable: Thrives in a fast-paced, dynamic environment where collections and risk strategies evolve rapidly.