Independent Credit Review & Model Risk Management Lead

The Independent Credit Review & Model Risk Management lead is a dual-responsibility portfolio operating within the Second Line of Defence. The successful candidate will independently evaluate the quality of credit underwriting, asset classification, and credit management practices to ensure adherence to internal guidelines and BNM’s Policy Document on Credit Risk. Concurrently, the role is tasked with governing the institution's Model Risk Management Framework, providing rigorous pre-implementation and periodic post implementation validation for all credit and risk scorecards, IFRS 9 / MFRS 9 engines, and automated decision-making models.

This role provides an independent and countervailing perspective on credit and model-driven decisioning to the Senior Management and Board Committees.

Key Responsibilities

1. Independent Credit Review (ICR) Role (BNM Credit Risk Policy Alignment)

• Post-Approval Credit Reviews: Conduct regular, objective post-approval evaluations on a sampled portfolio of retail, SME, and/or corporate credit exposures to assess the quality of underwriting and credit appraisal soundness.

• Regulatory Compliance & Tracking: Formulate and update the Independent Credit Review Framework in compliance with the latest BNM Credit Risk directives. Document observations in Post-Approval Review (PAR) reports.

• Credit Rating Accuracy Verification: Review and challenge the accuracy of internal credit risk ratings and borrower classifications. Evaluate if underwriting structures remain aligned with the Board’s approved Risk Appetite Statement (RAS).

• Problem Credit & Restructuring Governance: Review flagged, non-performing, and Rescheduled & Restructured (R&R) credit facilities to detect signs of early warning deficiencies, verify staging accuracy (e.g., SICR triggers), and monitor controls aimed at preventing the "ever-greening" of loans.

• Stakeholder Escalation & Committee Reporting: Defend and debate PAR findings, systemic credit underwriting gaps, and action plans with senior stakeholders across Business Units and Credit Approval Authorities. Escalate findings directly to the Risk Management Committee (RMC) and Board Risk Management Committee (BRMC).

2. Model Risk Management (MRM) Role

• Model Validation Framework: Maintain and implement the institution's Model Risk standards and inventory. Define validation criteria across the entire model lifecycle.

• Pre-Implementation & Post-Implementation Validation: Perform rigorous independent validations of credit risk models (including PD, LGD, EAD rating systems, application/behavioral scorecards, and automated decision engines) prior to deployment and on an annual basis.

• Quantitative & Qualitative Challenging: Evaluate the integrity of input data, statistical methodology, calibration benchmarks, mathematical assumptions, and limitations of risk models.

• Performance Monitoring: Review and analyze model performance indicators—including rating migrations, Gini coefficients, PSI (Population Stability Index), KS statistics, override rates, and back-testing results.

• Model Governance & Documentation: Review and challenge technical model

development documentation to ensure methodology choices are sound and robust for external regulatory audits.

Job Requirements & Qualifications

• Education: Bachelor’s or Master's degree in a quantitative or financial field (e.g., Financial Engineering, Statistics, Mathematics, Actuarial Science, Finance, Banking, or Economics).

• Experience: Minimum of 8 years of banking or financial services experience specifically in Credit Risk Analytics, Model Validation, Risk Modeling, or Independent Credit Review (Internal Audit).

• Technical & Analytical Competencies:

o Deep, practical knowledge of Bank Negara Malaysia (BNM) policy documents

(specifically the Policy Document on Credit Risk and Capital Adequacy Framework).

o Familiarity with credit scorecards, MFRS 9 / IFRS 9 modeling methodologies (PD, LGD, EAD), and Basel internal rating requirements.

o Technical proficiency in data and statistical languages such as SAS, SQL, Python, or R is highly advantageous for model replication and data-driven analysis.

• Soft Skills: Highly developed analytical mindset, strong written presentation capability for committee paper formulation, and the professional confidence to present a countervailing voice against profit-generating business units

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