Financial Crime and Compliance department is responsible for ensuring that all financial crime and compliance risks within the Bank are identified, managed or mitigated. It establishes and maintains the Bank's regulatory communications, compliance risk management, regulatory reporting and compliance advisory. As an Assistant Vice President - Compliance Systems and Data Analytics, you will be responsible for designing and implementing end-to-end performance, optimisation and statistical validation of the Bank’s global AML and Compliance systems. You will bridge Financial Crime Compliance Risk Advisory, Data Engineering and Model Risk Management teams to transform AML systems from rule-based approaches to data-driven, risk-based detection models while ensuring full regulatory coverage. This is a full time permanent position.
Key Responsibilities
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Partner with the Financial Crime Subject Matter Experts (SMEs) to translate complex risk typologies into quantifiable statistical and logical rules
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Design and implement the rules in the AML and Compliance systems
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Develop, implement and maintain advanced segmentation methodologies to group customers by risk profile and transaction behaviour using statistical approaches (e.g. statistical clustering, peer group analysis and percentiles)
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Formally author and own comprehensive Model Documentation, including technical methodology designs, parameter tables and mapping lineage to mitigate financial crime risks and regulatory expectations
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Conduct regular diagnostic reviews of scenario performance using automated quantitative workflows
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Design and execute mathematical tuning strategies using Above-the-Line (ATL) and Below-the-Line (BTL) testing methodologies to ensure optimal threshold limits
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Perform look-back (back-testing) exercises using historical data to ensure that proposed threshold adjustments continue to align the Bank’s expectations and do not inadvertently drop or mask true suspicious behaviours
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Maintain automated Management Information (MI) suites, engineering interactive dashboards that track true-positive rates, alert-to-case conversion cycles and system health metrics
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Lead deep-dive data lineage and schema verification assessments between foundational upstream data lakes/feeders (e.g. core banking transaction streams) and downstream data consuming systems
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Validate technical system integration frameworks during major platform updates
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Establish and continuously verify data quality scripts to monitor data completeness, dropping records with scoring run errors or missing structural values before batch run execution
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Design comprehensive, automated testing suites for User Acceptance Testing (UAT) and Synthetic Data Testing to assess edge-case behaviours of new rules
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Conduct continuous market and regulatory reviews to ensure existing detection strategies remain compliant with evolving regulatory changes
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Pilot and integrate Machine Learning (ML) frameworks (such as isolation forests for unsupervised anomaly detection and decision trees for alert prioritisation) to augment traditional rule-based mechanisms
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Implement feedback loops that feed post-investigation outcomes directly back into analytical engines to iteratively refine model precision
Requirements
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Degree educated in Statistics, Data Science, Financial Engineering, Computer Science, or other relevant subject
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Demonstrated experience in Transaction Monitoring rule governance and/or related Transaction Monitoring activities
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Proven experience optimising and configuring automated compliance systems (e.g. SironAML, SironOne, Actimize, Mantas, or Quantexa)
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Proven experience applying statistical modelling, supervised/unsupervised machine learning, and data clustering to highly imbalanced financial datasets
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Experience in tuning and optimisation of financial crime and market abuse detection scenarios, including threshold calibration above and below current operational limits
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Hands-on experience in ATL and BTL testing methodologies
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Knowledge of Banking products
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Knowledge of JMLSG Guidance, POCA and MLRs, sanctions compliance and GDPR
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Advanced programming skills in Python
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Advanced database skills in SQL
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Familiarity with BI and MI tools and machine learning methodologies
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Excellent communication skills
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Fluency in Mandarin is a plus, but not mandatory
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Analytical and problem solving skills
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Attention to detail
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Team player
Having a presence in the UK for more than 90 years, Bank of China London Branch has been providing an excellent financial service to the market and maintain its competitive edge as an international bank operated in the UK. It has employees in over 33 countries, and it is one of the most internationalised overseas branch in the Bank of China Group. The Bank values the diversity of its employees and aims to create an inclusive environment where everyone can speak up and enjoy the equal opportunities to grow both personally and professionally.
