Lead Business Analyst – AI & Machine Learning

We are seeking a highly experienced Lead Business Analyst – AI & Machine Learning (Financial Crime) to lead the design, prioritization, and delivery of AI-driven solutions across AML, fraud, sanctions screening, transaction monitoring, and financial crime compliance. This role sits at the intersection of Financial Crime Compliance, Data Science, Technology, and Model Risk, translating regulatory obligations and risk typologies into scalable AI-enabled capabilities that materially reduce risk exposure while improving operational efficiency. The successful candidate will combine deep financial crime domain expertise with strong AI literacy and commercial acumen to ensure measurable impact in detection effectiveness, false positive reduction, and regulatory defensibility.

Key Responsibilities

1. Financial Crime AI Strategy & Value Realization

• Identify and prioritize AI use cases across:

• Transaction Monitoring

• Customer Risk Rating

• Sanctions & Name Screening

• Fraud Detection

• Adverse Media & KYC Automation

• Quantify business impact (false positive reduction, alert productivity, STR uplift, loss avoidance).

• Develop regulatory-defensible business cases and board-level materials.

• Establish KPIs: precision/recall, model stability, investigator productivity, regulatory compliance metrics.

2. AI Use Case Design & Typology Translation

• Translate emerging financial crime typologies into ML-ready problem statements.

• Convert regulatory findings and audit observations into data-driven solution requirements.

• Define feature requirements aligned to AML risk factors (behavioral, network, geographic, transactional).

• Partner with data teams to assess data sufficiency and lineage.

3. Model & Solution Delivery Leadership

• Lead detailed requirements for AI models including:

• Risk scoring logic

• Threshold design

• Segmentation strategy

• Explainability requirements

• Work closely with Data Scientists on:

• Feature engineering aligned to crime typologies

• Training/validation datasets

• Bias and fairness testing

• Ensure traceability from regulatory obligation → risk typology → model logic → operational outcome.

• Oversee UAT with investigators and compliance teams.

4. Governance, Model Risk & Regulatory Alignment

• Partner with Model Risk Management to ensure:

• Model documentation completeness

• Validation readiness

• Backtesting frameworks

• Ongoing performance monitoring

• Ensure compliance with:

• AML/CFT regulations

• Sanctions obligations

• Local MAS / FCA / OCC or equivalent regulatory expectations (depending on jurisdiction)

• Define explainability standards for regulatory reviews.

• Support responses to regulatory exams and audits.

5. Operating Model & Adoption

• Design AI-enabled investigation workflows.

• Define human-in-the-loop controls and escalation pathways.

• Lead change management across Financial Crime Operations.

• Track post-implementation value realization and model drift.

Required Qualifications

• 10+ years of experience in banking, with strong exposure to:

• AML / Financial Crime Compliance

• Transaction Monitoring

• Fraud Risk

• Sanctions

• Hands-on experience delivering AI/ML or advanced analytics solutions in regulated environments.

• Strong understanding of model performance metrics (precision, recall, AUC, false positive rate).

• Experience working with Model Risk Management frameworks.

• Demonstrated ability to engage regulators and senior compliance stakeholders.

• Strong quantitative and business case development skills.

Preferred Qualifications

• Experience in wholesale banking and complex corporate client environments.

• Familiarity with:

• Network analytics

• Graph-based detection models

• NLP for adverse media screening

• Behavioral anomaly detection

• Knowledge of MLOps and model lifecycle governance.

• MBA or advanced degree in Analytics, Data Science, Risk, or Finance.

Core Competencies

• Financial crime domain depth

• Regulatory fluency

• Structured analytical thinking

• Risk-based decision making

• Executive stakeholder management

• Cross-functional leadership

What Success Looks Like (12–18 Months)

• 25–40% reduction in false positives without loss of detection coverage.

• Measurable improvement in STR quality and typology coverage.

• Reduced investigation turnaround time.

• Regulatory examinations with no material findings related to AI models.

• Clear AI roadmap embedded into Financial Crime Strategy.