AI-Driven AML & Financial Crime Detection
WORKSHOPS · 05 · HALF-DAY COURSE
WORKSHOPS · 05 · HALF-DAY COURSE
AI-Driven AML & Financial Crime Detection
A 3.5-hour session for compliance leaders evaluating a transaction-monitoring or KYC workflow for AI integration. Participants leave with an implementation note addressing false-positive reduction and MAS-facing audit readiness.
You leave with
- RegTech integration map
- Audit-readiness checklist
- Implementation note
- False-positive reduction path
Duration
3.5 hours, single session
Format
In-person or virtual
Cohort size
Max 20 participants
Requires
Working knowledge of AML, sanctions or KYC operations
Facilitator
Designated MLRO, CAMS, CFCS; 15+ years banking, payments, fintech
Delivery record
30% fewer false positives, 35% faster review turnaround
CPD / CPE hours
4 CPD hours
Certificate on completion
Certificate of Completion, Praxora Lab
Overview
Regulators are moving from encouraging AI in compliance to specifying how it must be governed
The Monetary Authority of Singapore issued a consultation in November 2025 proposing sector-wide AI Risk Management Guidelines, covering generative AI and AI agents, for all financial institutions, building on its 2024 thematic review of how banks already use AI. The pressure driving that shift is measurable: LexisNexis Risk Solutions' True Cost of Financial Crime Compliance study puts annual compliance costs across Asia-Pacific at US$45 billion, with costs rising for ninety-eight percent of surveyed institutions in the most recent year measured, and industry estimates reported by Retail Banker International put false-positive rates on rule-based transaction-monitoring alerts as high as ninety to ninety-five percent, the specific problem machine-learning anomaly detection is meant to address without weakening genuine detection.
Jason Lee built this course's implementation approach from fifteen years as MLRO and MAS liaison, not a RegTech vendor's sales deck: a thirty percent reduction in false positives and a thirty-five percent improvement in review turnaround, achieved on his own team. Participants leave with an integration map scored against the workflow they bring into the room and a MAS-facing audit-readiness checklist for what an inspection actually checks, not a generic compliance framework.
Why Praxora
Why this, not a platform
An implementation note your compliance team can act on, not a RegTech sales pitch.
-
Practitioner-led, not faculty-assigned
Jason Lee, MLRO and MAS liaison of fifteen years, teaches this course because he ran the transaction-monitoring floor himself.
-
A named framework, not a licensed template
Built on the same RegTech integration patterns behind his own 30 percent false-positive reduction, not a generic compliance primer.
-
One real, produced output — not a case study
Every session ends with a drafted implementation note for the AML, sanctions or KYC workflow you bring in.
-
A small, capped cohort
Max 20 participants, so the room works from a real workflow, not a hypothetical audit.
-
Built on institutions that already trust it
Built from fifteen years as MLRO and MAS liaison, working directly against what a MAS inspection actually checks.
-
One focused session — not an 18-week course
3.5 hours, one session. No cohort waitlist, no months of drip-fed modules.
Key takeaways
What participants leave with
-
An integration map of RegTech options, Chainalysis, Elliptic and ComplyAdvantage among them, scored against the workflow in the room.
-
A MAS-facing audit-readiness checklist for what an inspection actually checks.
-
A drafted implementation note the compliance function can act on immediately.
-
A concrete path to reducing false positives without weakening detection.
See a full sample of these documents › Filled out for a fictional company, so you can judge the format before enquiring.
Objective
Evaluate a transaction-monitoring or KYC workflow for AI and machine-learning anomaly-detection integration, and draft an implementation note a compliance function can act on.
Participants bring a real AML, sanctions or KYC workflow. The session works through RegTech integration patterns, Chainalysis, Elliptic and ComplyAdvantage among them, drawn from Jason Lee's own delivery record of a 30 percent reduction in false positives and a 35 percent improvement in review turnaround.
Bridging deep technical expertise with business alignment to drive compliance efficiency, security and scalability.
Why this course
Every analyst I have ever managed has, at some point, told me the same thing in different words: that chasing false positives all day is what makes them stop trusting the alert queue altogether. That is the problem this course is actually about. Not AML in the abstract, the specific, quiet cost of a compliance function where good people learn to click through warnings because most of them turn out to be nothing.
I have spent fifteen years as MLRO and MAS liaison, and the thirty percent false-positive reduction I cite on this course's page is not a vendor's marketing number, it is what my own team achieved by being deliberate about where machine learning anomaly detection actually helps and where it does not. I built my career on the belief that compliance done well is not a cost centre slowing the business down, it is the function that lets a regulated institution move faster because it is not drowning in noise. If someone leaves this session with one honest implementation note for their own transaction-monitoring workflow, they will have a better afternoon than the one I usually get to have explaining this to a room.
Syllabus
Four segments across 3.5 hours
Left with an implementation note for one real workflow, addressing false-positive reduction and MAS-facing audit readiness.
-
20 min
Framing
The workflow in the room, and where false positives cost the most.
- One real AML, sanctions or KYC workflow, named up front
- Where in that workflow false positives cost the analyst team the most trust
- What a MAS-facing inspection would already flag about it
OutputWorkflow brief
-
55 min
RegTech integration patterns
For AML and anomaly detection.
- RegTech integration patterns for machine-learning anomaly detection
- Chainalysis, Elliptic and ComplyAdvantage, scored against the workflow in the room
- Where anomaly detection actually reduces noise, and where it just relocates it
OutputIntegration options mapped to the workflow
-
15 min
Break
-
1 hr
MAS-facing audit readiness
What an inspection actually checks.
- What a MAS inspection actually checks, versus what teams assume it checks
- Audit-trail discipline for every automated flag and override
- Building the checklist from fifteen years as MLRO and MAS liaison, not a template
OutputAudit-readiness checklist
-
1 hr
Close
A drafted implementation note.
- The integration map and audit-readiness checklist brought together
- A drafted implementation note the compliance function can act on immediately
OutputImplementation note
Who should attend
MLROs, compliance leads and risk officers
-
MLROs & Heads of Compliance
MLROs and Heads of Compliance at banks, payment institutions, fintechs and insurers.
-
Sanctions & KYC/KYB leads
Sanctions, KYC/KYB and transaction-monitoring leads under MAS licensing.
-
Risk & technology leaders
Risk and technology leaders evaluating a RegTech integration decision.
Facilitator
Jason Lee
Head of Compliance & AI-Driven AML Specialist
Head of Compliance and MLRO with fifteen-plus years across banking, payments, fintech and digital assets, integrating AI and machine learning anomaly detection into AML, sanctions and transaction monitoring frameworks for regulated financial institutions.
Read the full profile ›Common questions
Before you enquire
-
Is this suitable for someone new to compliance, or only experienced practitioners?
Working knowledge of AML, sanctions or KYC operations is assumed, since the session works directly on a real workflow you bring. It is not an introduction to compliance fundamentals.
-
Does the session recommend specific RegTech vendors?
It works through integration patterns, using platforms including Chainalysis, Elliptic and ComplyAdvantage as worked examples of how AI and machine-learning anomaly detection get integrated, not as a vendor recommendation for your specific procurement.
-
Can this be run for a compliance team as a corporate in-house session?
Yes. A dedicated session for one organisation's compliance function, working on that function's own workflows, is available at a fixed programme fee.
-
How does this relate to AI Governance & Compliance for Regulated Industries, the integrated programme?
This course is one of the three pillars in that one-day programme, run alongside Terence Kok's governance course and Sameen Khan's data-governance course. Regulated financial institutions building a full compliance-AI roadmap should look at the integrated programme instead.
-
Is there a discount for sending more than one person, or booking more than one course?
Yes. Organisations sending leadership through more than one course, or through a course and its integrated programme, get a combined rate rather than separate bookings. See the corporate and bulk enrolment section on the workshops page, or raise it directly in your enquiry.
-
What if I need to cancel, defer or a session is rescheduled?
Full terms for individual, open-cohort and corporate in-house bookings, including deferral windows and what happens if Praxora Lab cancels a session, are set out in the refund and cancellation policy.
-
What happens after the session ends?
Every cohort joins a standing network of practitioners and business leaders: ongoing networking events, fireside chats, and case study interviews with business owners and C-suite leaders on AI projects they've recently completed. The named facilitator also stays the point of contact for questions on applying what was covered.
Industry engagement
A network that keeps working after the workshop ends
Every cohort at Praxora Lab joins a standing network of practitioners and business leaders, not a one-off attendee list. Alumni are invited back for ongoing networking events, fireside chats with industry experts, and case study interviews with business owners and C-suite leaders on AI projects they have recently completed, a running view of what adoption actually looks like once the workshop room empties.
The learning experience
A small cohort, led by the practitioner who built the course
Every session is capped at a small cohort and taught in person by the practitioner who built it, not a teaching assistant working from someone else's slides. Participants work against one real team's own workflow rather than a generic case study, and leave with a draft they can put to use immediately.
A named facilitator, not a support desk, stays the point of contact after the session for questions on applying what was covered.
Investment
Open cohort or corporate in-house
Open cohort
On enquiry
Per participant, max 20 participants
- Certificate on completion: Certificate of Completion, Praxora Lab
- Integration options mapped to your workflow
- Audit-readiness checklist
- Drafted implementation note
Corporate in-house
On enquiry
Fixed programme fee, max 20 participants
- Everything in the open cohort
- Dedicated session for one organisation's compliance function
- Worked on that function's own AML, sanctions or KYC workflows
- Fixed fee, not priced per head
- Group rates with the AI Governance & Compliance for Regulated Industries integrated day
Full cancellation, deferral and refund terms are set out in the refund and cancellation policy.
Enquire about upcoming dates
