Senior Officer, Compliance Analytics and Modelling

Job Purpose

Responsible for designing, operating, and developing data- and AI-driven compliance and risk monitoring activities to enable early detection, timely alerts, and mitigation of risks related to legal violations, internal policy breaches, and other risks that may adversely impact the Bank/Organization.

Key Accountabilities (1)

1. Data-Driven Compliance and Risk Monitoring

• Design and implement monitoring scenarios based on transaction data, customer data, internal operational data, and other relevant data sources.
• Perform periodic and ad-hoc data analysis in order to:
• Detect early signs of violations of laws, regulations, and internal policies;
• Identify compliance risks, conduct risks, fraud risks, and abnormal behaviors.
• Monitor and assess compliance risk trends through indicators, alerts, and analytical dashboards.

2. Alert Analysis and Handling

• Review, analyze, and validate alerts generated from monitoring systems and data analytics tools.
• Conduct preliminary assessments, identify root causes, and evaluate the severity of compliance risks.
• Coordinate with relevant units (Business, Operations, IT, Legal, Risk Management, etc.) to clarify, address, and track compliance issues.

3. Development of Monitoring Models, Indicators, and Tools

• Develop and continuously enhance:
• Compliance Key Risk Indicators (KRIs);
• Data-driven monitoring logic, rules, and models.
• Proactively propose automation solutions, advanced analytics, AI/ML, and Generative AI applications for compliance monitoring.
• Coordinate with Technology/Data units to standardize data sources and improve data quality for compliance purposes.

4. Development and Application of AI Agents in Compliance

• Participate in the design, development, and operation of AI Agents for compliance activities, including:
• AI Agents supporting the review of data, transactions, and compliance records;
• AI Agents for automatic aggregation, classification, and prioritization of risk alerts;
• AI Agents supporting pattern analysis, trend detection, and root cause identification of risks.
• Translate compliance and risk management requirements into AI use cases, monitoring logic, and AI Agent workflows.
• Coordinate with relevant units to ensure AI Agent implementation:
• Complies with model governance and technology risk management requirements;
• Ensures data security, AI ethics, and regulatory compliance.

5. Reporting and Management Advisory

• Prepare periodic and ad-hoc compliance monitoring reports for Senior Management and relevant governance committees.
• Provide assessments, early warnings, and recommendations to prevent and mitigate compliance risks.
• Participate in advising improvements to processes, policies, and internal controls based on data and AI-driven insights.

6. Continuous Improvement and Capability Development

• Stay updated on laws, regulations, and best practices related to compliance, AML, anti-fraud, data governance, and AI.
• Participate in internal training and knowledge-sharing initiatives on data-driven compliance and AI Agent applications.

Key Relationships - Internal Stakeholders

Corporate Assurance, Banking Administration Divisions & Functional stakeholders within and across divisions and senior management teams

Key Relationships - External Stakeholders

The State Bank and other Regulatory agencies, Financial Information Analysis units & Police
Industry partners/ Correspondent banks

Success Profile - Qualification and Experiences

1. Education and Experience

• Bachelor’s degree or higher in Finance & Banking, Accounting & Auditing, Risk Management, Law, Information Technology, Data Science, or related fields.
• Experience in Compliance, Risk Management, Internal Audit, Data Analytics, AML/Fraud Monitoring.
• Experience in banking, financial services, or fintech is preferred.

2. Technical Skills

• Strong data analysis capability and logical thinking.
• Proficiency in data analysis tools (e.g., SQL, Power BI/Tableau; Python/R is an advantage).
• Experience in designing and developing AI Agents for business use cases is an advantage, particularly in compliance and risk management.
• Knowledge of:
• AI/LLM operating principles;
• Prompt engineering, agent workflows, Retrieval-Augmented Generation (RAG), and tool/function calling at an applied level.

3. Supporting Skills

• Strong reporting, presentation, and data-driven recommendation skills.
• Ability to collaborate cross-functionally and manage stakeholders.
• Proactive mindset toward innovation, experimentation, and technology adoption.

BEHAVIORAL COMPETENCIES

• Integrity, independence, and objectivity in compliance activities.
• Data-driven and AI-enabled mindset in risk identification and management.
• Strong risk awareness, with clear understanding of AI limitations and emerging risks (e.g., bias, hallucination, model risk).