Description
We are seeking an experienced Financial Crime Analytics Assistant Vice President to develop and enhance analytics capabilities that identify, prevent and disrupt money mule activity across the bank’s customer base and payment networks.
The successful candidate will use innovative data analytics and machine learning techniques to extract valuable insights from the bank's data reserves, leveraging these insights to inform strategic decision-making, improve operational efficiency, and drive innovation across the organisation.
Accountabilities
- Identification, collection, extraction of data from various sources, including internal and external sources.
- Performing data cleaning, wrangling, and transformation to ensure its quality and suitability for analysis.
- Development and maintenance of efficient data pipelines for automated data acquisition and processing.
- Design and conduct of statistical and machine learning models to analyse patterns, trends, and relationships in the data.
- Development and implementation of predictive models to forecast future outcomes and identify potential risks and opportunities.
- Collaborate with business stakeholders to seek out opportunities to add value from data through Data Science.
Assistant Vice President Expectations
- To advise and influence decision making, contribute to policy development and take responsibility for operational effectiveness. Collaborate closely with other functions/ business divisions.
- Lead a team performing complex tasks, using well developed professional knowledge and skills to deliver on work that impacts the whole business function. Set objectives and coach employees in pursuit of those objectives, appraisal of performance relative to objectives and determination of reward outcomes.
- Demonstrate a clear set of leadership behaviours to create an environment for colleagues to thrive and deliver to a consistently excellent standard.
Requirements
- Advanced experience analysing large, complex datasets to identify patterns, trends and emerging financial crime risks.
- Proven experience within Fraud, Financial Crime, Anti-Money Laundering (AML), Credit Risk or other risk analytics functions, with an analytical focus.
- Advanced proficiency in SQL and Python and/or SAS for data extraction, transformation, modelling and analytical insight generation.
- Degree in a STEM discipline (e.g. Mathematics, Statistics, Computer Science, Engineering) or equivalent quantitative analytical experience.
Desirable Skills
- Banking experience, with an understanding of customer account and transaction data.
- Understanding of controls, governance, model risk management, and regulatory compliance within a highly regulated environment.
- Ability to communicate complex analytical findings clearly to both technical and non-technical stakeholders.