What We Can Offer
• The job holder continuously upgrade Credit Risk models with new source of data, advanced and suitable modeling techniques;
• The job holder drives Credit Score & Risk Model Usage for better customer experience and increase competitiveness of the bank;
• The job holder drives collaboration for risk culture with consistent risk metrics as common language across organization;
• Transform data to insights to support decision making in credit risk management by conducting data manipulation, analytic & modeling with different data visualization & analysis techniques;
• Develop Credit Risk related models (including but not limited to Basel IRB, IFRS9 and Pillar 2 Stress Testing models) for the measurement of PD, EAD and LGD for Retail and/or Non-Retail, include but not limited to statistical / advanced AI/ML models;
• Develop other types of Risk Models including but not limited to Fraud Risk Model, Collection & Recovery model, and collaborate with Data & Analytics Division to develop non-risk models such as Propensity model, Churn model...
• Support continuous improvement efforts through research on new & updated techniques, process and domain.
• Execute end to end model life cycle steps including but not limited to data preparation, model development & validation, model deployment, model operationalization;
• Adopting best coding standards and automation to help create coding repositories for various methods used across modelling team
• Ensure that models are fit for purposes not only for regulatory estimates but also for daily business usage, underwriting decisions, risk appetite decisions and strategy design.
• Participate in relevant model implementation and its user acceptance test to ensure models are appropriately implemented not only within the direct system environment but also its relevant downstream environments.
• Drive credit risk model usage by communicating to Senior Management & Business Units on the Credit Risk Model Design & Framework; collaborating with credit policy & product development team in designing credit decision strategies for specific program/products;
• Solid knowledge of statistical modelling and econometric methods;
• Experience in statistical programming: (SAS, Python, R, SPSS…);
• Experience communicating complex analysis and models across a diverse team;
• Knowledge of Regulatory and Risk management (e.g. Basel, IFRS9, Stress Testing, ECL etc.), experience of working in Banking Risk Analytics;