Live Online Advanced AML / CFT & Financial Crime Analytics Training Course
Live online, instructor-led · next cohort Oct 19, 2026 · from USD 1,700
Rule-based transaction monitoring was built for a slower world, and it struggles against mule networks recruited online, layering through mobile wallets, invoice manipulation in trade finance and funds hopping between blockchains. Institutions that generate thousands of alerts yet file few useful reports now face hard questions from supervisors, correspondent banks and FATF assessors about whether their controls work. Over ten days, experienced compliance and analytics staff learn to rebuild detection on evidence. Early sessions cover customer risk scoring, behavioural clustering, scenario design, above-the-line and below-the-line threshold testing, fuzzy name matching for sanctions screening, and investigation analytics that end in a usable suspicious transaction report.
The second week moves into entity resolution and graph analysis with NetworkX and Neo4j, anomaly detection and gradient boosting for alert prioritisation, SHAP explanations, validation and drift monitoring, plus dedicated work on trade-based laundering, virtual asset tracing and terrorist financing. Labs use Python, SQL and synthetic payment data. Money laundering reporting officers, monitoring leads, FIU analysts, fraud data scientists and bank examiners gain most. You leave able to design, tune and defend a detection programme. Classroom, online and in-house formats lead to a CPD-accredited certificate.
At a Glance
How Live Online Training Works
Live online training, led by a trainer in real time, for participants anywhere.
More about live online training →- Format
- Live online, instructor-led
- Duration
- Mon - Fri (10 Days)
- Fee
- USD 1,700 (KES 160,000)
- Next start dates
- Oct 19, 2026, Nov 16, 2026, Dec 14, 2026
- Certificate
- Certificate of Completion
- What you need
- A computer with a reliable internet connection, a webcam and a headset
About this course
Supervisors increasingly judge anti-money laundering and counter-terrorist financing regimes on effectiveness rather than paper compliance. For a bank, payment service provider or mobile money operator, that means proving that monitoring scenarios reflect the institution's own risk assessment, that thresholds were set from data rather than habit, that any machine learning in use is explainable and independently validated, and that alerts become intelligence a financial intelligence unit can act on. Analysts who can carry out this work remain scarce.
The Advanced AML / CFT & Financial Crime Analytics Training Course is a ten-day programme for practitioners who already understand customer due diligence and suspicious transaction reporting. Days one to five strengthen the core analytical toolkit: financial crime data and lineage, statistical customer risk scoring and peer-group segmentation, scenario design and calibration, sanctions and PEP screening analytics, and investigation work from triage to a filed report. Days six to ten go further, into transaction graphs and entity resolution, supervised and unsupervised learning, model risk management, and specialist typologies covering trade-based money laundering, virtual assets, terrorist financing and mobile money agent networks.
Each day includes a lab on synthetic data in Python, pandas, SQL or a graph database, so participants practise the methods rather than watch them. The programme closes with an integrated project in which teams design a detection programme for a fictional institution and defend it before a mock supervisory panel.
Training Objectives
On completing the ten days, participants will be able to:
- Build an analytics-ready financial crime dataset from core banking, card, mobile money and trade finance sources, with documented lineage and quality checks.
- Design statistical customer risk scores and behavioural peer groups that can be back-tested against past case outcomes.
- Calibrate transaction monitoring scenarios through above-the-line and below-the-line testing and justify each threshold with evidence.
- Evaluate sanctions and PEP screening performance by tuning fuzzy matching and measuring missed and false matches.
- Apply entity resolution and graph analytics to expose mule networks, funnel accounts and layered ownership structures.
- Develop machine learning models that prioritise alerts while keeping their reasoning visible to investigators and supervisors.
- Validate detection rules and models through outcomes analysis, drift monitoring and champion-challenger testing.
- Detect trade-based laundering, virtual asset flows and terrorist financing patterns with specialist analytical tests.
What you'll learn
Key Takeaways
Participants take back to their institutions:
- A back-tested customer risk scoring model with documented feature weights and segment definitions.
- A threshold tuning workbook recording above-the-line and below-the-line results for each scenario.
- A fuzzy matching test harness for measuring sanctions screening accuracy.
- Reusable Python notebooks for transaction graphs, community detection and mule ring discovery.
- A working alert prioritisation model with SHAP explanations that investigators can read.
- A model validation report template covering data, conceptual soundness, outcomes and drift.
- Analytical checklists for trade invoices, blockchain tracing and terrorist financing indicators.
- A team-built detection programme design and roadmap, reviewed by a mock supervisory panel.
Training Methodology
Teaching is analytical and hands-on, and each concept is tested on data the same day. Participants work through:
- Daily coding labs in Python, pandas, scikit-learn, NetworkX and SQL on synthetic banking, mobile money and trade data
- Graph database exercises in Neo4j for link analysis and entity resolution
- Worked case files reconstructed from published FATF typology reports
- Peer critique of tuning decisions, model documentation and STR narratives
- A mock supervisory review in which teams defend their monitoring design
- Pre-built notebooks so that participants with lighter coding experience can still complete every lab
Day 1: Financial Crime Data Foundations and Lineage
- What FATF effectiveness assessments test and how that reshapes analytics priorities
- Mapping core banking, card, mobile money and trade finance data into one monitoring model
- Data lineage and reconciliation between source systems and the monitoring engine
- Quality tests on KYC, transaction and counterparty fields that silently weaken detection
- Building customer, account and transaction tables in SQL for analysis
- Lab: profiling a transaction extract in Python and pandas
- Agreeing the analytical questions the MLRO, board and FIU need answered
Day 2: Statistical Customer Risk Scoring and Segmentation
- Limits of static risk-rating matrices and expert-assigned weights
- Statistical and weighted approaches to customer risk scoring
- Behavioural features: velocity, cash intensity, counterparty spread and cross-border share
- Peer-group segmentation with k-means and hierarchical clustering
- Detecting outliers within segments using robust z-scores
- Back-testing scores against past STRs, account exits and law enforcement requests
- Lab: building and back-testing a customer risk model
- Documenting the scoring methodology for examiners
Day 3: Scenario Design and Threshold Calibration
- Turning typologies into scenario logic: structuring, rapid pass-through, round-tripping and dormant account reactivation
- Coverage mapping between scenarios and the institutional risk assessment
- Above-the-line and below-the-line testing in practice
- Setting thresholds from segment distributions and percentiles
- Alert productivity measures: case conversion, STR conversion and investigator effort
- Change control and audit trail for every tuning decision
- Lab: recalibrating a structuring scenario on sample data
Day 4: Sanctions, PEP and Adverse Media Analytics
- The UN Security Council consolidated list alongside regional and national sanctions lists
- Fuzzy matching with Levenshtein, Jaro-Winkler and phonetic algorithms
- Transliteration and name-order variation in Arabic, Amharic and Cyrillic names
- Tuning match thresholds and measuring missed and false matches
- Payment message screening and the richer data carried in ISO 20022
- Ownership and control tests for indirectly sanctioned entities
- Adverse media screening with text analytics
- Lab: building a fuzzy matching test harness
Day 5: Investigation Analytics and STR Quality
- Risk-based alert triage and prioritisation
- Flow-of-funds and source-and-application-of-funds analysis
- Timelines and counterparty summaries built from raw account statements
- Structuring STR narratives so FIU analysts can act on them
- goAML reporting fields and structured data submission
- Quality assurance sampling of closed alerts and cases
- Tipping-off risk and confidentiality during investigations
- Lab: from alert to STR on a mobile money case
Day 6: Entity Resolution and Network Analytics
- Entity resolution across customers, accounts, devices, phone numbers and addresses
- Building transaction graphs in NetworkX and Neo4j
- Centrality, community detection and cycle detection for layering and round-tripping
- Identifying money mule networks and funnel accounts
- Tracing beneficial ownership chains through shell companies
- Presenting network findings to investigators and prosecutors
- Lab: uncovering a mule ring in a synthetic payments dataset
Day 7: Machine Learning for Detection and Alert Prioritisation
- Where machine learning adds value and where rules remain the better tool
- Label scarcity, class imbalance and using investigation outcomes as labels
- Feature engineering from rolling windows, ratios and graph metrics
- Anomaly detection with isolation forests and autoencoders
- Gradient boosting for alert scoring and false positive reduction
- Evaluation with precision-recall curves and lift rather than accuracy
- Explaining model outputs to investigators with SHAP values
- Lab: training an alert prioritisation model in scikit-learn
Day 8: Model Risk Management and Validation
- Model inventory, risk tiering and documentation standards
- Independent validation: conceptual soundness, data integrity and outcomes analysis
- Population stability, drift and performance monitoring after go-live
- Champion-challenger testing and parallel running before rules are retired
- Fairness checks and the financial inclusion risk of over-flagging customer groups
- Preparing models for supervisory and internal audit review
- Lab: writing a validation report for the Day 7 model
Day 9: Specialist Typologies: Trade, Virtual Assets and Terrorist Financing
- Trade-based money laundering: over-invoicing, under-invoicing, phantom shipments and unit price analysis
- Using mirror trade statistics such as UN Comtrade to test declared values
- Virtual asset tracing: address clustering, mixers, cross-chain bridges and exchange exposure
- The FATF Travel Rule and the data virtual asset service providers must share
- Terrorist financing signals: small-value transfers, abuse of non-profit organisations and hawala
- Mobile money agent networks and cash-in, cash-out anomalies
- Lab: unit price anomaly testing on trade transactions
- Lab: following funds on a public blockchain explorer
Day 10: Integrated Detection Programme Project
- Project brief: a mid-sized bank with retail, mobile money and trade finance books
- Building a detection coverage map from the institutional risk assessment
- Combining rules, models and network analytics in a single alert pipeline
- Effectiveness KPIs and key risk indicators for the board and supervisor
- Implementation roadmap, skills plan and data requirements
- Team presentations to a mock supervisory panel
- Peer review, personal action plan and course close
Participants should already work with AML/CFT controls or financial crime data. Typical roles include:
- Money laundering reporting officers and their deputies
- Transaction monitoring and sanctions screening managers
- Financial crime data analysts and data scientists in banks and fintechs
- Financial intelligence unit analysts and strategic analysis teams
- Investigators in bank financial crime units, anti-corruption agencies and police economic crime units
- Compliance staff at mobile money operators, payment service providers and remittance companies
- Central bank and capital markets supervisors who examine AML/CFT systems
- Model risk and internal audit professionals who review monitoring models
- Compliance teams at virtual asset service providers
A CPD-accredited Certificate of Completion issued by Vision Reach Global Consultancy is awarded to participants who attend the full ten days, complete the daily labs and contribute to the integrated detection project.
The certificate records the course title, dates and delivery mode, while the notebooks, tuning workbook and validation report built during the programme serve as practical evidence of advanced analytical competence.
Choose Where You Want to Learn
Advanced AML / CFT & Financial Crime Analytics Training Course Venues
KenyaNairobiNext Nov 9· 13 dates$3,000- KenyaMombasaNext Oct 26· 13 dates$3,200
- KenyaNaivashaNext Nov 2· 13 dates$3,000
- KenyaKisumuNext Oct 26· 13 dates$3,000
- KenyaMachakosNext Nov 2· 13 dates$3,200
UgandaKampalaNext Nov 2· 13 dates$3,800
RwandaKigaliNext Oct 26· 13 dates$3,800
TanzaniaArushaNext Nov 9· 13 dates$3,800- TanzaniaDodomaNext Oct 26· 13 dates$3,600
- TanzaniaDar es SalaamNext Oct 26· 13 dates$4,000
- TanzaniaZanzibarNext Nov 16· 13 dates$5,200
NigeriaLagosNext Nov 16· 13 dates$5,600- NigeriaAbujaNext Nov 9· 13 dates$5,600
South AfricaPretoriaNext Nov 2· 13 dates$6,400- South AfricaJohannesburgNext Nov 2· 13 dates$6,800
- South AfricaCape TownNext Oct 26· 13 dates$7,600
UAEDubaiNext Oct 26· 13 dates$7,600
GhanaAccraNext Nov 16· 13 dates$9,000
Scroll sideways to see all 18 cities →
Frequently Asked Questions About Live Online Advanced AML / CFT & Financial Crime Analytics Training Course
Everything you need to know about this course before you register.
By the end of the Advanced AML / CFT & Financial Crime Analytics programme, you'll be able to build an analytics-ready financial crime dataset from core banking, card, mobile money and trade finance sources, with documented lineage and quality checks, design statistical customer risk scores and behavioural peer groups that can be back-tested against past case outcomes, calibrate transaction monitoring scenarios through above-the-line and below-the-line testing and justify each threshold with evidence, and evaluate sanctions and pep screening performance by tuning fuzzy matching and measuring missed and false matches. The full breakdown of topics is covered session by session in the Course Outline tab above.
Participants should already work with AML/CFT controls or financial crime data. Typical roles include: Money laundering reporting officers and their deputies, Transaction monitoring and sanctions screening managers, Financial crime data analysts and data scientists in banks and fintechs, Financial intelligence unit analysts and strategic analysis teams, Investigators in bank financial crime units, anti-corruption agencies and police economic crime units, Compliance staff at mobile money operators, payment service providers and remittance companies, Central bank and capital markets supervisors who examine AML/CFT systems, Model risk and internal audit professionals who review monitoring models, and Compliance teams at virtual asset service providers.
Advanced AML / CFT & Financial Crime Analytics Training Course typically runs as 10 Days. It's available as in-person classroom, live virtual, and in-house corporate training — every course can also be delivered on-site for your team on dates that suit you.
Advanced AML / CFT & Financial Crime Analytics Training Course is scheduled in-classroom in Nairobi, Kenya, Mombasa, Kenya, Naivasha, Kenya, and Kisumu, Kenya, and 14 other locations, plus a live interactive virtual classroom you can join from anywhere. Check the schedule panel above for exact upcoming dates and fees in each location.
The next live virtual cohort of Advanced AML / CFT & Financial Crime Analytics starts November 16, 2026, with new classroom cohorts also running on a rolling basis. Pick a date and location in the schedule panel above, then click "Register for the Course" — it takes a few minutes and your seat is confirmed once payment or a signed purchase order is received.
Yes — delegates who meet the attendance requirement receive a Certificate of Completion for Advanced AML / CFT & Financial Crime Analytics Training Course from Vision Reach Global Consultancy, issued in the name you register with, so double-check the spelling at checkout.
Advanced AML / CFT & Financial Crime Analytics Training Course is pitched at advanced professionals. If you're unsure whether it's the right fit for your current role or background, message our training advisors before you register and they'll help you confirm.
Fees for Advanced AML / CFT & Financial Crime Analytics Training Course vary by delivery location and format and are shown in real time in the schedule panel above once you pick a date. Register 3 or more delegates on the same course together and a 5% team discount is applied automatically — larger cohorts can request a custom corporate quote.
Yes — Advanced AML / CFT & Financial Crime Analytics Training Course can be delivered on-site at your offices (or virtually for distributed teams), with case studies and examples tailored to your industry and the specific challenges your team is working through. Switch to the "In-House" tab in the schedule panel above to request a proposal.
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