About the Role
Contributing to the integrity of the bank's risk management framework, this role delivers technical support in model monitoring and validation activities to ensure ongoing compliance with internal and regulatory standards. By executing routine model performance checks and escalating anomalies, it safeguards the reliability of risk analytics used in business decision-making. The position works within established procedures and under moderate supervision, resolving standard issues independently while referring exceptions to senior colleagues. Outputs directly support the Central Risk team's ability to meet reporting obligations and maintain robust risk controls, with communication focused on direct stakeholders and the immediate team.
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Key Responsibilities:
- Reliability of risk model outputs through timely execution of scheduled model monitoring activities
- Regulatory compliance in model monitoring processes aligned with internal and external standards
- Data integrity and traceability in risk model monitoring datasets
- Business insight for risk management decisions enabled by clear and actionable model monitoring reports
Responsibilities
1. Reliability of risk model outputs through timely execution of scheduled model monitoring activities
- Conduct periodic monitoring of IFRS 9 and other risk models using established statistical and business performance metrics.
- Compile and validate model performance data to identify deviations from expected outcomes, ensuring early detection of potential issues.
- Document findings and observations in accordance with internal audit and regulatory requirements.
- Escalate significant anomalies or emerging risks to the manager for further investigation and action.
Performance Measures:
- Consistency and completeness of model monitoring documentation
- Accuracy of anomaly detection and escalation
- Stakeholder confidence in reliability of model performance reporting
2. Regulatory compliance in model monitoring processes aligned with internal and external standards
- Apply current regulatory guidelines (e.g., MAS, PRA, RBI) to daily model monitoring tasks, ensuring adherence to all relevant standards.
- Maintain up-to-date records of model monitoring activities for audit and regulatory review.
- Support the preparation of regulatory submissions by providing validated model performance data as required.
- Identify and report process gaps or compliance risks to the manager for remediation.
Performance Measures:
- Absence of regulatory findings in model monitoring processes
- Quality of records available for audit and regulatory inspection
- Timeliness and accuracy of data provided for regulatory submissions
3. Data integrity and traceability in risk model monitoring datasets
- Extract, cleanse, and organise model input and output data from risk systems to support monitoring activities.
- Verify data lineage and traceability to ensure all datasets used in monitoring are accurate and complete.
- Maintain clear audit trails for all data transformations and calculations performed during monitoring.
- Collaborate with IT and data management teams to resolve data quality issues impacting model monitoring.
Performance Measures:
- Completeness and accuracy of data used in model monitoring
- Clarity and auditability of data transformation documentation
- Resolution rate of data quality issues impacting monitoring
4. Business insight for risk management decisions enabled by clear and actionable model monitoring reports
- Prepare concise monitoring reports summarising model performance, key findings, and recommended actions for direct stakeholders.
- Translate technical results into business-relevant insights for risk and finance teams.
- Respond to queries from business users regarding model monitoring outputs and methodologies.
- Support the continuous improvement of reporting templates and communication channels for model monitoring results.
Performance Measures:
- Clarity and relevance of monitoring reports for business stakeholders
- Responsiveness to stakeholder queries and feedback
- Demonstrated impact of monitoring insights on risk management actions
Qualifications
- Bachelor's degree in Statistics, Mathematics, Finance, Economics, or related field
- Relevant certifications in risk management or data analytics (e.g., FRM, CFA, SAS Certification) preferred
- Ongoing professional development in regulatory and risk management practices
Experience Requirements
Requires at least 3 years of experience in risk analytics or model monitoring within banking or financial services, with exposure to risk systems such as SAS, Python, or similar platforms.
