The role is responsible for independently validating Anti-Financial Crime (AFC) models in accordance with the bank's model validation standards. The candidate will produce comprehensive reports, assess model design and methodology, and provide actionable recommendations to strengthen AFC risk management frameworks.
Key Responsibilities:
Model Validation
- Independently validate AFC models against the bank's model validation standards.
- Evaluate the robustness of model design, development methodology, and data integrity.
- Conduct thorough reviews of development code to ensure alignment with documented methodologies and results.
- Review and assess follow-up actions taken by model owners in response to prior validation issues.
- Communicate validation outcomes clearly, translating technical findings for non-technical stakeholders.
- Collaborate with regional teams to prepare regular reports on model performance for senior management.
- Actively contribute to enhancing model validation standards and frameworks.
Team Contributions
- Stay updated on evolving regulatory requirements, industry best practices, and analytical advancements in AFC risk management.
- Propose updates to model development standards in consultation with the team lead.
- Assist in planning and coordinating the model validation calendar.
- Support initiatives related to systems, infrastructure, and databases used for analytic projects.
Requirements
- Undergraduate degree in a quantitative field.
- 58 years of experience in analytics/technology, with 23 years in advanced data analytics preferred.
- Prior experience in large-scale analytics projects.
- Experience or familiarity with analytics related to AFC/AML/compliance risks.
- Ability to manage multiple concurrent projects in a fast-paced environment.
Technical Skills
- Proficiency in Python and/or R, including data wrangling and machine learning libraries (e.g., Pandas, Keras, Tensorflow, Sklearn).
- Proficiency in SQL and visualization tools such as Qlik is an advantage.
- Comfortable working with structured/unstructured data and distributed databases.
- Familiar with natural language processing and network link analysis.
- Knowledge of model development and validation best practices.
