Global Financial Crimes - Transaction Monitoring, AVP - Hybrid (Irving, TX)

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This is a hybrid position. The selected colleague will work at an MUFG office or client sites up to four days per week and work remotely the remaining day(s). A member of our Talent Acquisition team will provide more details.

Job Summary:
Reporting into the Head of Financial Crimes Transaction Monitoring, this new position has been created for the purpose of establishing GFCD Operations Intelligence & Analytics capabilities for BTMU.
One of several key positions in the new Global Financial Crimes Division Operations function, this position will be responsible for (1) helping to identify detection scenarios, (2) provide analytics support for developing, tuning, optimizing and modifying segmentation to improve transaction monitoring and screening systems, (3) customer segmentation (4) helping to coordinate the implementation of scenarios, which detect financial crime and (5) create statically representative samples to ensure that risk is not missed below the threshold. The incumbent is responsible for supporting leadership to ensure a globally consistent effort that enables continuing improvement to the transaction monitoring capabilities and further mitigate potential financial crime.

Major Responsibilities:

• In connection with the Global Financial Crimes program

• Develop mathematical or statistical theory to interpret customer KYC data and historical transaction data to group customers and non-customers into segments in order to monitor their activity in the correct thresholds

• Identify relationships and trends of historical transactional data for clustering for AML transaction monitoring

• Analyze the clusters/relationships and present the information to Senior Managers in the data visualization tools

• Create statistical representative samples for ATL/BTL testing in order to validate that the transaction monitoring model is effective and functioning accordingly.

• Process large amount of data for statistical modeling and graphic analysis.

• Perform model validation, memorializing model selection rationales and defined assumptions.

• Develop and test experimental designs, sampling techniques, and analytical methods in order to monitor new typologies and emerging risks.

• Develop data mining methodologies, including logistic regression, random foresr, xgboost and Bayesian networks

• Help develop of models involving tuning, calibration, segmentation and optimization.

• Support the development of policies and procedures for AML transaction monitoring life cycle, including reviews of scenario validation, segmentation and optimization tools.

• Support large strategic optimization and segmentation program to enhance and tune MUFG 's GFCD Transaction Monitoring Program.

• Recommend customer segmentation and optimization for MUFG 's GFCD monitoring system across multiple lines of business.

• Coordinate with regional Intelligence & Analytics teams to implement on the global model.

• Work collaboratively across functional teams within GFCD to ensure effective and efficient operations with clearly defined roles and responsibilities.

Experience/Skills

• Knowledge of mathematics particularly statistics.

• Ability to code using R or Python for customer segmentation and data analytics.

• Ability to solve problems through mathematical and deductive reasoning.

• 3-5 years ' experience designing, analyzing, testing and/or validating BSA/AML models, and/or OFAC sanctions screening models.

• Familiarity implementing, testing or evaluating performance of financial crime and compliance systems.

• Proven track record of strong quantitative testing and statistical analysis techniques as it pertains to BSA/AML models, including name similarity matching, classification accuracy testing, unsupervised/supervised machine learning, neural ne