Senior Collections Risk Analyst job at FairMoney
Posted by: great-volunteer
Posted date: 2025-May-17
Location: Kampala, Uganda
Senior Collections Risk Analyst 2025-05-16T08:04:53+00:00 FairMoney https://cdn.ugashare.com/jsjobsdata/data/employer/comp_7445/logo/FairMoney.png https://fairmoney.io/ FULL_TIME Kampala Kampala 00256 Uganda Banking Management 2025-05-30T17:00:00+00:00 Uganda 8 Description FairMoney is a pioneering mobile banking institution specializing in extending credit to emerging markets. Established in 2017, the company currently operates primarily within Nigeria, and it has secured nearly €50 million in funding from renowned global investors, including Tiger Global, DST, and Flourish Ventures. FairMoney maintains a strong international presence, with offices in several countries, including France, Nigeria, Germany, Latvia, the UK, Türkiye, and India. In alignment with its vision, FairMoney is actively constructing the foremost mobile banking platform and point-of-sale (POS) solution tailored for emerging markets. The journey began with the introduction of a digital microcredit application exclusively available on Android and iOS devices. Today, FairMoney has significantly expanded its range of services, encompassing a comprehensive suite of financial products, such as current accounts, savings accounts, debit cards, and state-of-the-art POS solutions designed to meet the needs of both merchants and agents. To gain deeper insights into FairMoney's pivotal role in reshaping Africa's financial landscape, we invite you to watch this informative video. 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.
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.
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.
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.
Benefits - Private Health Insurance
- Pension Plan
- Training & Development
- Hybrid work
- Paid Time Off
Recruitment Process - Screening interview with a Senior Recruiter- 30 minutes.
- Technical Assessment
- Technical Interview with the Lead Risk Manager for 45-60 minutes.
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. 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. 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. 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. 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. JOB-6826f1a5afa18 Vacancy title:
Senior Collections Risk Analyst Jobs at:
FairMoney Deadline of this Job:
Friday, May 30 2025 Duty Station:
Kampala | Kampala | Uganda Summary
Date Posted: Friday, May 16 2025, Base Salary: Not Disclosed JOB DETAILS: Description FairMoney is a pioneering mobile banking institution specializing in extending credit to emerging markets. Established in 2017, the company currently operates primarily within Nigeria, and it has secured nearly €50 million in funding from renowned global investors, including Tiger Global, DST, and Flourish Ventures. FairMoney maintains a strong international presence, with offices in several countries, including France, Nigeria, Germany, Latvia, the UK, Türkiye, and India. In alignment with its vision, FairMoney is actively constructing the foremost mobile banking platform and point-of-sale (POS) solution tailored for emerging markets. The journey began with the introduction of a digital microcredit application exclusively available on Android and iOS devices. Today, FairMoney has significantly expanded its range of services, encompassing a comprehensive suite of financial products, such as current accounts, savings accounts, debit cards, and state-of-the-art POS solutions designed to meet the needs of both merchants and agents. To gain deeper insights into FairMoney's pivotal role in reshaping Africa's financial landscape, we invite you to watch this informative video. 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.
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.
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.
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.
Benefits - Private Health Insurance
- Pension Plan
- Training & Development
- Hybrid work
- Paid Time Off
Recruitment Process - Screening interview with a Senior Recruiter- 30 minutes.
- Technical Assessment
- Technical Interview with the Lead Risk Manager for 45-60 minutes.
Work Hours: 8 Experience in Months: 36 Level of Education: bachelor degree Job application procedure
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