Manager- Surveillance Systems & Optimization

Job description

Do you want your voice heard and your actions to count? 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. With a vision to be the world’s most trusted financial group, it’s part of our culture to put people first, listen to new and diverse ideas and collaborate toward greater innovation, speed and agility. This means investing in talent, technologies, and tools that empower you to own your career. Join MUFG, where being inspired is expected and making a meaningful impact is rewarded. About The Role Position Title: Global Financial Crimes Design, Tuning and Optimization Corporate Title: AVP Internal Title: Manager Reporting to: Vice President Location: Bangalore Job Profile Purpose of Role This position will be responsible for the design, calibration, optimization, and ongoing validation of trade surveillance and transaction monitoring (TM) scenarios across securities products. This role ensures the effectiveness of surveillance controls in detecting market abuse, financial crime risks, and anomalous trading behaviors, while balancing alert productivity, regulatory expectations, and operational efficiency. The individual will work closely with Compliance, Surveillance Operations, Technology (e.g., Actimize/Siron/KX), and Model Governance teams to enhance detection capabilities through data-driven tuning, scenario refinement, and advanced analytics. Main Responsibilities • In coordination with Global and Regional Financial Crimes– • Perform periodic tuning of AML transaction monitoring and trade surveillance scenarios (e.g., spoofing, layering, wash trades, insider trading, front running). • Analyze alert volumes, false positives, and productivity metrics to recalibrate thresholds and parameters. • Conduct back testing and sensitivity analysis across historical datasets to assess scenario performance. • Identify over-/under-triggering scenarios and propose data-driven tuning recommendations. • Support scenario de-scoping, consolidation, or enhancement based on risk coverage and efficiency. • Design, implement, and optimize securities-focused TM scenarios covering: Equity, fixed income, derivatives, and structured products. • Ensure monitoring coverage across key risk typologies (e.g., market abuse, AML risks, sanctions evasion, cross-border trading risks). • Perform deep-dive analysis on transaction datasets to assess behavioral patterns and anomalies by leveraging advanced analytics. • Develop and test experimental designs, sampling techniques, and analytical methods in order to monitor new typologies and emerging risks. • Conduct BTL (Below-the-Line) and ATL (Above-the-Line) testing to assess detection effectiveness and missed risk. • Develop data mining methodologies, including logistic regression, random foresr, xgboost and Bayesian networks • Support the development of policies and procedures for AML transaction monitoring life cycle, including reviews of scenario validation, segmentation and optimization tools. • Recommend customer segmentation and optimization for MUFG’s GFCD monitoring system across multiple lines of business. • Develop KPIs/KRIs to measure surveillance effectiveness (e.g., hit rate, conversion rate, review time). • Partner with Operations to balance alert volumes with investigative capacity. • Identify opportunities to automate surveillance processes and reporting (e.g., dashboards using Tableau). • Pilot advanced techniques such as: Machine learning–based anomaly detection and LLM-based data enrichment or pattern extraction Candidate Profile- Skills and knowledge • Ability to apply mathematical principles or statistical approaches where needed to solve problems • 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. • Strong knowledge about model risk management and associated regulatory requirements • Prior experience in designing compliance program tuning and configuration methodologies, including what-if detection scenario analytics, excess over threshold, and sampling ATL/BTL testing. • Ability to code using R or Python for customer segmentation and data analytics preferred. • Familiarity with vendor models like Hotscan, Actimize SAM/WLF, KX , Siron , Search Space, RDC, Bridger Insight, ACE Pelican, TCH OFAC Screening (EPN), FICO Credit/Debit, Guardian Analytics, and Threat Metrix. • Ability to perform parameterization and threshold calibration of existing scenarios through analysis of underlying trades/orders/quotes data, alerts closure data, feedback from internal and external stakeholders • Design new scenarios and/or modify existing scenario logics to enhance coverage, drawing lessons from enforcement cases, gap analyses of industry publications e.g., internal and external feedback • Drive enhanced surveillance via enablement of automated monitoring via our monitoring system, fine-tuning filters, parameter thresholds to improve the quality of alerts generated • Ability to implement customer segmentation using clustering algorithm for optimization of alert generation • Experience in alert risk scoring project to risk rate the alerts generated in order to reduce false positives • Ability to work with country teams to roll out customised anti-money laundering and fraud scenarios covering corporate banking an