Credit Risk Portfolio Monitoring and Governance

Job Summary

This role plays a critical part in strengthening the Bank’s credit risk management framework through credit risk modeling, portfolio monitoring, risk analytics, stress testing, and scenario analysis across Retail and Wholesale portfolios. The position is responsible for developing and maintaining credit risk methodologies and models, including IFRS 9 / TFRS 9 ECL, PD, LGD, EAD, scorecards, and credit rating models, while monitoring portfolio performance and assessing risk impacts under various scenarios.

The role combines quantitative modeling, portfolio analytics, and business-oriented risk insights to support effective risk management and strategic decision-making. Working closely with management, regulators, and key stakeholders, the position delivers data-driven analysis, portfolio risk assessments, and actionable recommendations that strengthen the Bank’s risk management capabilities and portfolio resilience.

Role and Responsibilities / หน้าที่ความรับผิดชอบ

Credit Risk Model Development and Maintenance

• Prepare and analyze internal and external data for model development, calibration, maintenance, and implementation.
• Develop, calibrate, and maintain credit risk models using appropriate statistical and quantitative techniques.
• Develop and enhance credit scorecards, including Application, Behavioral, and Bureau Score models, as well as Internal Credit Rating (ICR) models for corporate and large-exposure borrowers.
• Analyze credit risk drivers, borrower behavior, portfolio characteristics, and emerging risk trends.

• Monitor model performance, portfolio characteristics, and data quality; investigate material changes; and perform recalibration, enhancement, or redevelopment where appropriate.

IFRS 9 / TFRS 9 Credit Risk Modeling and ECL

• Develop, calibrate, and maintain IFRS 9 / TFRS 9 credit risk parameters, including PD, LGD, and EAD, across retail and non-retail portfolios.
• Assess model assumptions, portfolio characteristics, macroeconomic impacts, and other factors relevant to IFRS 9 / TFRS 9 methodologies.
• Perform monthly risk parameter and ECL calculations, including reasonableness checks, reconciliation, movement analysis, and investigation of material changes.
• Review and enhance methodologies, assumptions, parameters, and analytical processes in response to model performance, portfolio developments, economic conditions, business needs, or regulatory requirements.

Governance, Regulatory, and Business Support

• Maintain model documentation covering methodologies, data, assumptions, results, limitations, monitoring outcomes, and material changes.
• Support model governance reviews, internal and external audits, and regulatory examinations by preparing analyses, documentation, presentations, and responses to relevant questions or findings.
• Collaborate with Risk Management, Business Units, Finance, Data and Technology, and other stakeholders on credit risk modeling and analytical initiatives.
• Perform ad-hoc credit risk and portfolio analyses to support risk management, business operations, and decision-making.
• Translate quantitative analyses and model results into clear business insights, risk implications, and recommendations for technical and non-technical stakeholders.
• Explore machine learning, AI, and automation to enhance model development, maintenance, and analytics where appropriate.

Qualifications / คุณสมบัติ

• Bachelor's degree or higher in Economics, Finance, Statistics, Engineering, Computer Science, Data Science, Mathematics, or related fields.
• 1-3 years of experience in Risk Management, Analytics, Data Science, Finance, Banking, Consulting, or related fields.
• Fresh graduates with strong quantitative, analytical, and problem-solving capabilities are encouraged to apply.

Specific knowledge and skill / ความรู้เฉพาะตำแหน่ง

• Strong quantitative, analytical, and problem-solving skills, with a good foundation in statistical analysis and data analytics.
• Knowledge of credit risk modeling, IFRS 9 / TFRS 9, statistical modeling, or banking risk management is an advantage.
• Proficiency in analytical and programming tools such as SAS, Python, SQL, or equivalent tools.
• Experience with Databricks is a plus.
• Strong communication and stakeholder management skills, with the ability to present complex topics effectively to diverse audiences. 
• Intellectual curiosity, learning agility, and a willingness to challenge conventional thinking.
• High ownership, resilience, and a proactive approach to problem solving.