Manager - Transaction Monitoring Tuning & Optimization

Position : Manager - Sanctions Screening Tuning & Optimization

Location : Bangalore (Hybrid Model)

Experience : 7 - 15 Years

Job Description :

Purpose of Role :

Global Financial Crimes Design, Tuning and Optimization AVP. This position will be responsible for (1) helping to identify detection scenarios, (2) providing 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 that detect financial crime and (5) creating statically representative samples to ensure that risk is not missed below the threshold.

The incumbent may also be called upon to support entity screening tuning and/or customer risk rating tuning. 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.

Main Responsibilities :

- In coordination with Global and Regional Financial Crimes.

- 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 above/below-the-line (ATL/BTL) testing in order to validate that the transaction monitoring model is effective and functioning accordingly.

- Process large amounts 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 forest, xgboost and Bayesian networks.

- Help develop 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.