Job Description

Key Result Areas

Supporting Actions

AML Transaction Monitoring & Pattern Detection

Lead comprehensive AML transaction monitoring programs across all business verticals. Develop and implement advanced pattern detection algorithms to identify suspicious transaction patterns, layering activities, and structuring behaviors. Design automated alert generation systems with intelligent threshold calibration to optimize detection rates while minimizing false positives. Conduct periodic effectiveness reviews of monitoring scenarios and update detection rules based on emerging typologies and regulatory guidance. Collaborate with Compliance and Legal teams for suspicious transaction reporting (STR) preparation and regulatory submissions

Fraud Analytics & Anomaly Detection

Establish robust fraud analytics frameworks for real-time and batch fraud detection across Insurance claims, Investment transactions, Digital Lending applications, Payment transactions, and Super App activities. Develop machine learning models for anomaly detection including unusual transaction patterns, account takeover attempts, identity fraud, and application fraud. Create behavioral profiling systems to identify deviations from normal customer patterns and flag high-risk activities. Implement automated fraud scoring mechanisms with dynamic risk rating based on multiple parameters. Prepare detailed fraud trend analysis reports and collaborate with business units on fraud prevention strategies

Operational Risk Dashboards & Automation

-Design and deploy comprehensive operational risk dashboards providing real-time visibility into risk metrics, control effectiveness, and emerging risk trends. Automate recurring risk reporting processes including KRI tracking, control testing results, incident monitoring, and risk event analysis. Develop self-service analytics capabilities enabling business units and risk teams to access relevant risk data and insights on demand. Implement predictive analytics models to identify potential operational failures, process breakdowns, and control gaps before materialization. Establish governance frameworks for dashboard maintenance, data quality validation, and user access management

Compliance Risk Analytics & Reporting

Develop data-driven compliance monitoring frameworks tracking adherence to regulatory requirements across all business verticals. Create automated compliance dashboards monitoring key compliance metrics, regulatory deadline tracking, obligation fulfillment, and breach identification. Conduct periodic compliance effectiveness assessments using data analytics to identify control gaps and areas requiring remediation. Prepare comprehensive compliance reports for senior management, board committees, and regulatory submissions. Analyze regulatory changes and assess impact on existing monitoring frameworks, recommending necessary enhancements.

Data-Driven Control Insights & Recommendations

Analyze control testing results and monitoring data to derive actionable insights on control effectiveness and improvement opportunities. Develop evidence-based recommendations for control enhancements, process improvements, and risk mitigation strategies. Prepare executive presentations and reports translating complex analytical findings into business-relevant insights. Conduct root cause analysis on control failures, compliance breaches, and risk events to recommend preventive measures. Establish feedback loops ensuring recommended controls are implemented and effectiveness is validated through ongoing monitoring.

Cross-Functional Analytics Collaboration

Partner with Insurance, Investment, Digital Lending, Payments, and Super App business teams to understand risk profiles and analytics requirements. Coordinate with IT and Data Engineering teams for data integration, system enhancements, and analytics platform development. Collaborate with Internal Audit, Compliance, Legal, and Regulatory Affairs on audit support, compliance validation, and regulatory response preparation. Provide analytics expertise and insights to business strategy discussions, product launches, and process redesign initiatives. Facilitate knowledge sharing across business verticals on best practices, emerging risks, and effective control measures.

Advanced Analytics & Machine Learning Applications

Research and implement cutting-edge analytics techniques including machine learning, artificial intelligence, and predictive modeling for risk detection. Develop unsupervised learning models for unknown pattern discovery and emerging risk identification. Create supervised learning models for classification, prediction, and risk scoring applications. Implement natural language processing (NLP) capabilities for unstructured data analysis including customer communications, complaint analysis, and document review. Stay current with industry best practices, regulatory technology (RegTech) innovations, and analytics tool advancements