Head Of Credit Risk

Position

Head of Credit Risk & Data Science

Department

Credit Risk & Data Science

Location

Global

About FINN

FINN is a fintech company building simple and transparent products that improve financial well-being for underbanked employees. Our core offering gives people access to wages they have already earned, alongside tools that help them manage money better, build savings, access insurance, earn additional income, and use fairer financial services.

Founded in 2022, FINN is the largest player in the early wage access space in Southeast Asia and is expanding rapidly into new markets globally. We are looking for ambitious builders who take ownership, move fast, and want to shape products that create meaningful impact in people's lives.

About The Role

We are looking for a Head of Credit Risk to own and lead FINN's credit risk strategy, underwriting policies, and portfolio performance as we expand across Southeast Asia and other markets.

This is a senior risk leadership role focused on balancing sustainable growth, responsible lending, and portfolio profitability. You will own the end-to-end credit risk lifecycle, from risk appetite and underwriting strategy to portfolio monitoring, credit policy optimization, and loss management.

You will leverage data science and machine learning to strengthen credit decisioning, improve risk segmentation, and expand access to financial services for underserved customers. You will lead a team of risk analysts and data scientists, ensuring that models and analytical tools translate into effective credit policies and measurable business outcomes.

Working closely with Product, Engineering, Finance, and executive leadership, you will establish a scalable, data-driven credit risk function that supports FINN's growth while maintaining strong portfolio quality.

Key Responsibilities

• Credit Risk Strategy & Portfolio Management
• Own FINN's credit risk strategy, risk appetite, underwriting policies, and approval frameworks across products and markets.
• Develop and continuously optimize credit policies, risk thresholds, and customer segmentation strategies to balance approval rates, credit losses, and risk-adjusted returns.
• Own portfolio performance monitoring, including delinquency, roll rates, default rates, loss rates, and other key credit risk indicators.
• Establish early warning systems to identify emerging portfolio risks and implement timely corrective actions.
• Lead portfolio reviews, risk assessments, scenario analysis, and stress testing to evaluate portfolio resilience under different economic and business conditions.
• Partner with Finance on expected credit loss modeling, provisioning, and portfolio-level financial risk assessments.
• Ensure credit risk policies are appropriate for each market and aligned with applicable regulatory requirements and responsible lending standards.
• Develop and maintain clear risk governance frameworks, escalation processes, and reporting for executive leadership.
• Underwriting & Credit Decisioning
• Own the end-to-end underwriting strategy, including customer eligibility, credit assessment, approval rules, and risk-based decisioning.
• Improve approval rates and customer conversion while maintaining target default rates and portfolio quality.
• Develop and refine risk segmentation strategies to identify underserved customer segments with attractive risk-adjusted returns.
• Use portfolio performance data and customer behavior to continuously improve underwriting policies and credit decisions.
• Design and evaluate controlled experiments, policy tests, and champion-challenger strategies to measure the impact of underwriting changes.
• Partner with Product and Engineering to implement scalable, automated credit decisioning processes that reduce time-to-decision and friction in the customer journey.
• Evaluate alternative data sources, credit bureaus, and external partnerships to strengthen risk assessment and improve coverage of underserved populations.
• Data Science & ML-Driven Risk Management
• Lead the application of data science and machine learning to credit scoring, underwriting, risk segmentation, and fraud detection.
• Define model requirements and work with data scientists and engineers to develop, validate, deploy, and continuously improve credit risk models.
• Ensure models translate into effective underwriting policies and measurable improvements in approval rates, losses, and risk-adjusted portfolio performance.
• Establish appropriate model monitoring, validation, interpretability, fairness, and drift detection practices.
• Oversee model experimentation, performance evaluation, and ongoing optimization across products and markets.
• Ensure credit decisioning systems are scalable,