Senior Risk Analyst Retail Products

1. Analyze data to assess risks & Monitor input data used from the bank's system:

• Analyze complex data from customer behavior of borrowing and opening cards to understand potential risks, identify customer images of the product portfolio.
• Monitor internal and external data to find factors affecting the risk level of the customer group opening cards or disbursing, proactively identify early or potential risks.
• Synthesize data from different sources to provide a comprehensive assessment of risk factors, supporting decision making.
• Develop and deploy effective systems and processes to collect and store data, ensuring accessibility for future analysis projects.

2. Design and maintain a risk reporting system:

• Create and maintain a portfolio risk management reporting system that ensures timely and continuous provision of information related to risk warning indicators.
• Conduct thorough and consistent daily, weekly, monthly and quarterly loan portfolio quality assessments to ensure compliance with risk management standards.

3. Prepare data Data for advanced analytics

• Prepare and clean data for advanced analytics such as A-score, B-score, or clustering.
• Prepare data for validation or deploying models into the system.

4. Perform assessment or participate in the development of machine learning models:

• Have knowledge of KS, GINI, PSI, AUC, Confusion matrix, and some other statistical indicators in credit scoring models.
• Have knowledge or experience in machine learning or credit scoring models in the banking and finance sector.

• Proficient in SQL Server, Excel and risk analysis reports.
• Familiar with programming languages such as R or Python.
• Have experience or knowledge related to quantitative or machine learning models
• Have experience in designing loan portfolio quality reports.
• Experience in building models to determine PD, LGD or EAD indicators according to IFRS9.
• Analytical and problem solving skills.
• Excellent communication and presentation skills.
• Meticulous with a focus on accuracy and reliability in data analysis.