GEN AI - Transaction Monitoring Investigation

About the role

The AML Business Unit Specialist is the bridge between the operational business and the technical delivery team on anti-money laundering and transaction monitoring AI initiatives. This role is for domain experts with deep AML/compliance expertise who understand how transaction monitoring systems actually work in practice and can translate that operational reality into guidance for AI agents.

Specialists work within delivery teams, ensuring that AI systems are designed with a keen understanding of business processes, regulatory constraints, edge cases, and the human expertise that keeps AML systems compliant and effective.

The expectation is active domain leadership: validating AI functionalities against real-world operational needs, defining business rules that agents will use for risk decisioning, curating training data that reflects authentic AML scenarios, and maintaining the feedback loop between operations and engineering throughout development and hypercare phases.

Position Responsibilities

Business Process Expertise & Validation

• Validate functional design against business reality: ensure that AI agent functionalities (transaction pattern analysis, red flag identification, risk categorization, summarization, escalation decisions) align with actual operational needs and compliance requirements
• Refine target-state workflows in collaboration with business analysts and design teams: iterate and validate “to-be” workflows, focusing on how agentic AI will operate within or alongside existing AML teams
• Identify exception and edge case scenarios that require human intervention, policy exceptions, or represent high risk — essential for guiding agent behavior, fallback design, and escalation logic
• Validate that proposed AI approaches meet regulatory expectations and banking/compliance standards

Process & Control Design Input

• Participate actively in design reviews: translate business requirements into functional and technical requirements with clarity
• Contribute to design changes necessitated by limitations in solutioning or technical architecture — balancing operational feasibility, control points, and compliance alignment
• Define business rules, thresholds, red flags, and decisioning logic that the AI system will use for transaction assessment, risk flagging, and escalation
• Ensure that transaction monitoring controls, audit trails, and governance structures are maintained or enhanced in the AI-enabled design

Data, Document, and Use Case Input

• Review, curate, and generate representative transaction monitoring examples: provide input on synthetic data generation covering real-life AML scenarios — from typologies in production to edge cases
• Support AI training data quality: validate labelled transaction data, suggest refinements based on domain nuance, and highlight language, structure, or contextual signals that indicate compliance concerns or legitimacy red flags
• Advise on data representativeness: ensure training datasets reflect the diversity of transaction types, customer profiles, geographies, and risk patterns that the system will encounter in production
• Document real-world commentary and decision rationale from operational teams to inform AI reasoning

Continuous Validation and Feedback Loop

• Participate in prototype evaluation and fine-tuning phases: provide domain feedback on early AI behavior, alert detection patterns, and rule firing logic
• Support hypercare activities: validate that the AI system behaves as expected against live or near-live transaction data, flag drift or unexpected patterns
• Bridge UAT design: define meaningful test scenarios by providing business-relevant transaction cases and articulating what correct AI behavior should look like
• Capture feedback from end-user teams during pilot phases and translate it into refinements for the AI system

Stakeholder Engagement & Communication

• Maintain communication with operations and AML teams: act as the voice of end users, ensuring their pain points, regulatory constraints, and operational priorities are understood throughout development
• AML Domain SME responsibility: provide insights on industry practices, regulatory guidance, and banking standards to inform functional design, technical architecture, and delivery approach
• Translate between business and technical teams: explain operational constraints to engineers, explain technical limitations to business stakeholders
• Support training and change management: help operations teams understand how to work with the AI system and where human judgment remains critical

Additional information

This role is for AML and transaction monitoring experts who want to shape how AI agents operate in regulated financial services. You’ll work at the frontier of AI-driven compliance, ensuring that systems designed in laboratories actually work in the messy, complex reality of transaction monitoring operations.

Your domain expertise is the difference between an AI system that sounds good in theory and one that financial institutions can actually trust and deploy. If you have deep AML knowledge, understand operational reality, and want to guide AI systems toward compliance and effectiveness, this is where that expertise matters most.

Core Requirements

• 7+ years in anti-money laundering, transaction monitoring, or AML compliance roles — with direct experience in operational AML teams, compliance functions, or AML-focused consulting
• Deep understanding of transaction monitoring systems, alert investigation processes, and AML rule design and tuning
• Strong knowledge of banking regulations and AML compliance frameworks (BSA/AML, FATF guidelines, regional/local regulatory requirements)
• Experience validating and refining AML processes, workflows, and control designs
• Proven ability to translate operational requirements into functional and technical specifications
• Strong communication and stakeholder management skills — comfortable engaging with operations teams, compliance officers, technologists, and executives
• Understanding of AI/ML concepts sufficient to discuss limitations, training data quality, and AI behavior with technical teams
• Experience with transaction typology analysis, red flag identification, and AML risk assessment methodologies

Additional Strong Signals

• Experience with AML software platforms (FICO, Actimize, Murex, or equivalent)
• Background in leading AML process improvement or system implementation initiatives
• Familiarity with AI/ML in financial services, particularly in risk and compliance contexts
• Experience curating, labelling, or validating training data for financial or compliance systems
• Knowledge of model governance, fairness assessment, and bias mitigation in high-stakes financial applications
• Published work or thought leadership in AML, transaction monitoring, or financial crime prevention
• Understanding of data residency, data privacy, and compliance requirements in multi-jurisdictional AML operations
• Experience with UAT design and validation in regulated financial services environments
• Bachelor’s degree in Finance, Economics, Compliance, Computer Science, or a related field
• Advanced certifications (CAMS, AML/CFT, or equivalent) highly valued