Senior Analyst/ Manager/ AVP- Transaction Monitoring, Tuning & Optimization

Mitsubishi UFJ Financial Group · Bengaluru

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Discover your opportunity with Mitsubishi UFJ Financial Group (MUFG), one of the world’s leading financial groups. Across the globe, we’re 150,000 colleagues, striving to make a difference for every client, organization, and community we serve. We stand for our values, building long-term relationships, serving society, and fostering shared and sustainable growth for a better world.

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Join MUFG, where being inspired is expected and making a meaningful impact is rewarded.

About the Role

Position Title: Global Financial Crimes Transaction Monitoring

Corporate Title: Analyst/ AVP

Internal Title: Sr Analyst/ Manager/ AVP

Reporting to: Vice President

Location:  Bangalore
 

Job Profile

Purpose of Role

This new position has been created for the purpose of establishing GFCD Global TM capabilities for MUFG.

One of several key positions in the Global Financial Crimes Division Transaction Monitoring function, this position will be responsible for providing strategic execution of the AML control globally for MUFG.

Main Responsibilities

• In connection with the Global Financial Crimes program –

• Ability to independently manage, organize, and prioritize multiple tasks, projects and responsibilities
• Develop mathematical or statistical theory/models to interpret customer KYC data and historical transaction data to group customers and non-customers into segments and clusters in order to monitor their activity in the correct TM thresholds.
• Create statistical representative samples for ATL/BTL testing using hypergeometric sampling techniques 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.
• Conduct data profiling to validate relevant data and data sets to be used for customer segmentation and tuning initiatives.
• Support in the creation of detail documentation which includes scenario logic, identified tunable and static parameters and deminimus values for all parameters.
• Analyze data and sample to ensure accuracy of key data elements for alert generation

Candidate Profile

• Skills and knowledge

• 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.
• 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 networks, fuzzy logic matching, decision trees, etc.
• Familiarity of current compliance rules and regulations of the FRB, SEC, OCC, FATF, FinCEN, OFAC, and familiarity with USA PATRIOT Act, BSA/AML and OFAC screening regulations. 
• Prior experience designing compliance program tuning and configuration methodologies, including what-if detection scenario analytics, excess over threshold, and sampling above/below-the-line (ATL/BTL) testing.
• Working knowledge of one or more of the following programming platforms: SAS, Matlab, R, Python, SQL, VBA, etc.
• Familiarity with vendor models like Actimize SAM, SAS, SearchSpace, Mantas, OTUS, or SIRON.

• Education & professional qualifications

Bachelor's degree in statistics, mathematics, quantitative analysis, economics, computer science, data and technology Sciences or related fields is required. Advance degree a plus.

• Experience

8-10 years’ experience in designing, analyzing, testing and/or validating BSA/AML models, and/or OFAC sanctions screening models.

Mitsubishi UFJ Financial Group (MUFG) is an equal opportunity employer. We view our employees as our key assets as they are fundamental to our long-term growth and success. MUFG is committed to hiring based on merit and organsational fit, regardless of race, religion or gender.

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