Most financial institutions recognize the need for stronger control, better quality data, faster onboarding processes and more scalable delivery. The real challenge is deciding where to start, which activities to retain or outsource, which technologies should be used and how to make the change operational.
Banner image edited using AI.
This article focuses on the practical side of AML transformation. It explores how to map the AML value chain, redesign the operating model, select the right technology, pilot AI-enabled workflows and mobilize execution capacity where additional support is required. The approach can be adapted to asset managers, banks and other financial institutions alike.
The AML value chain is expanding in scope and complexity
The first step is to gain a clear view of your AML value chain. This means mapping key activities such as onboarding, due diligence, screening, risk assessment, monitoring, escalation, reporting and record keeping. For each step, you should understand who owns it, which system supports it, which control applies, and where delays or quality issues arise.
For Luxembourg asset managers, this is especially important, as the in-scope population may include investors, clients, assets, delegates and service providers. A practical review should identify where investor outreach occurs, how transfer agents and other providers contribute, where risk decisions are made, and where data is duplicated or manually reworked.
AML transformation starts with the operating model
Once your current state is clearly understood, practical choices can be made about the operating model. Should oversight of providers be strengthened? Is a hybrid model more appropriate? Should selected client-facing activities be insourced, or should internal capabilities be supported through technology and external capacity?
Your target model should clearly define roles and responsibilities. It should also include simple measures of success, such as onboarding turnaround time, quality findings and reporting timeliness.
Technology enablers are reshaping AML delivery
Technology should help solve real operational problems. If data is fragmented or difficult to maintain, priorities may include data collections and validation checks. If oversight is weak, dashboards, audit trails and exception reporting may be more valuable. The key is selecting the right combination for your operating model, data, controls and budget.
A practical selection process compares technology options against specific AML use cases, integration needs, reporting requirements and audit trail expectations.
Right foundations for AI and agents
AI should be used to support the AML workflow. Practical use cases include extracting data from KYC files, validating data through registry and data providers, identifying missing information, supporting screening review, drafting RFIs, summarizing case packs and triggering periodic review workflows.
Effective and secure use of AI requires clearly defined workflows, human review, quality measures, access controls and processes for managing incomplete or incorrect outputs.
Agentic AI can be explored where processes are mature and governance is clear. For example, an onboarding agent could prepare a case file, identify missing information, enrich approved data, draft an RFI, route the case for review and maintain a complete audit trail. The objective is not to replace accountability, but to reduce manual effort and make execution more consistent.
Managed AML services can help address scale and resilience
Managed AML services can play an important role where your operating model is clear; however, teams require capacity, specialized skills and resilience. This can support onboarding peaks, remediation backlogs, periodic reviews, customer outreach, case preparation, alert clearance, reporting or transformation delivery.
The value extends beyond additional capacity. A good managed service also brings governance, quality checks, clear SLAs, escalation routes, dashboards and continuous improvement.
What a practical AML transformation roadmap can look like
A practical roadmap may begin with a diagnostic review of your current AML value chain, followed by a target operating model, making decisions on what to retain or outsource, a technology and data plan, priority AI or automation pilots, and a governance framework to track outcomes.
Typical outputs include process maps, RACI matrices, KPI dashboards, technology requirements, technology options assessments, pilot designs, control procedures, migration plans, training materials and reporting packs.
Practical implications
Your AML transformation should result in visible operational improvements such as fewer manual hand-offs, clearer ownership, faster onboarding, better control evidence, more reliable reporting and a better foundation for regulatory engagement. Transformation initiatives should be assessed based on the outcomes delivered in practice: decisions made, processes redesigned, tools embedded, backlogs reduced and controls demonstrably improved.
How KPMG can assist
KPMG can help you move from assessment to execution. This includes mapping AML value chain, designing the target operating model, defining roles and controls, selecting and implementing technology where relevant, building dashboards, piloting AI-enabled workflows, managing transformation delivery and providing managed AML capacity for onboarding, reviews, remediation, screening or reporting where appropriate.
Contact our experts to discuss how we can support your AML transformation journey.
