Course Introduction
The Anti-Money Laundering Data Analytics Training Course provides a practical framework for using data analytics to strengthen financial-crime risk identification, transaction monitoring, customer risk assessment, investigation, case management, and AML management reporting. The program connects AML compliance concepts with modern analytical techniques, enabling participants to transform customer, account, transaction, geographic, behavioral, and case data into structured intelligence that can support risk-based AML programs.
Participants will learn how to work with diverse AML datasets, including customer profiles, beneficial ownership information, account relationships, transaction histories, customer risk ratings, sanctions and screening results, alerts, investigations, suspicious activity reports, case outcomes, and external risk indicators. Particular attention is given to data quality, entity resolution, historical transaction analysis, consistent risk classifications, data lineage, and analytical structures that enable compliance teams to develop a more complete understanding of customer and transactional activity.
The course emphasizes the risk-based approach that is central to the FATF Recommendations. Participants will learn how analytics can support the identification, assessment, understanding, and prioritization of money-laundering and terrorist-financing risks, while recognizing that analytical models should operate within an institution's wider governance, policies, controls, and regulatory obligations. FATF states that understanding ML/TF risks is fundamental to applying proportionate mitigation measures and prioritizing resources toward higher-risk areas.
A major component of the program is transaction monitoring analytics. Participants will examine transaction volumes, values, frequency, counterparties, locations, channels, account relationships, temporal patterns, and behavioral changes to identify unusual activity and investigate potential risk indicators. The course covers rules-based monitoring, threshold analysis, segmentation, anomaly detection, peer-group comparisons, behavioral baselines, alert analysis, and analytical approaches for reducing unnecessary investigative workload while maintaining appropriate risk coverage.
The program also introduces advanced AML analytics, including customer risk scoring, network and relationship analysis, anomaly detection, typology analytics, predictive modeling concepts, and case-prioritization techniques. Financial institutions can use technology and innovation to enhance AML compliance activities such as risk identification, transaction monitoring, and suspicious activity reporting, although analytical outputs require appropriate validation, governance, human review, and regulatory alignment.
By the end of the five-day program, participants will be able to develop an integrated AML analytics framework covering customer risk, transaction monitoring, alert performance, investigations, typologies, network intelligence, case analytics, reporting, and management dashboards. Practical exercises and a capstone project will enable learners to analyze realistic AML datasets, identify unusual patterns, develop risk indicators, investigate analytical alerts, build an AML intelligence dashboard, and present evidence-based findings to compliance, risk, audit, and senior-management stakeholders.
Duration
5 days
Who Should Attend
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AML analysts responsible for transaction monitoring, customer risk analysis, and financial-crime investigations.
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Compliance professionals seeking practical data analytics capabilities for AML and financial-crime programs.
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Transaction monitoring analysts investigating alerts, unusual activity, behavioral patterns, and customer transactions.
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Financial-crime investigators analyzing customer relationships, transaction flows, counterparties, and case information.
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MLROs and AML managers responsible for risk-based monitoring, escalation, investigations, and management reporting.
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KYC and customer due-diligence professionals analyzing customer profiles, risk indicators, ownership information, and behavioral changes.
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Risk analysts responsible for measuring financial-crime exposure and developing risk-based analytical frameworks.
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Data analysts working with customer, account, transaction, alert, case, and financial-crime datasets.
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Internal audit professionals reviewing AML data, monitoring processes, controls, analytical models, and reporting.
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RegTech and financial-crime technology professionals developing analytical solutions for AML compliance.
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Banking and financial-services operations professionals supporting transaction monitoring and investigative processes.
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Compliance executives and decision-makers seeking practical capabilities for data-driven AML risk management.
Course Objectives
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Develop a comprehensive understanding of AML data analytics and apply structured analytical techniques to customer, account, transaction, behavioral, geographic, and investigative data.
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Build reliable AML analytical datasets by integrating customer profiles, account relationships, transaction histories, risk ratings, alerts, investigations, cases, and relevant external risk indicators.
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Apply risk-based analytical methods to segment customers, accounts, products, jurisdictions, channels, and transaction activity according to relevant financial-crime risk characteristics.
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Analyze transaction behavior using volume, value, frequency, velocity, counterparties, geographic patterns, temporal relationships, and behavioral changes to identify unusual activity.
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Develop transaction-monitoring analytics using rules, thresholds, peer comparisons, behavioral baselines, anomaly indicators, and segmentation to support effective alert generation and review.
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Apply network and relationship analytics to investigate connections among customers, accounts, counterparties, businesses, beneficial owners, transactions, and other relevant entities.
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Analyze AML alert and case performance using volumes, conversion rates, dispositions, investigation times, escalation patterns, recurring typologies, and investigator workloads.
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Explore predictive and machine-learning concepts for anomaly detection, customer risk assessment, alert prioritization, case classification, and emerging financial-crime risk identification.
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Design interactive AML dashboards that communicate customer risk, transaction-monitoring activity, alert performance, investigations, typologies, geographic exposure, and management-level indicators.
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Complete an integrated AML analytics project involving data preparation, risk segmentation, transaction analysis, anomaly investigation, network intelligence, alert analytics, dashboard development, and management recommendations.
Comprehensive Course Outline
Module 1: AML Data Analytics and Financial-Crime Intelligence Foundations
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Understanding the role of data analytics in AML, CFT, customer due diligence, transaction monitoring, investigation, suspicious activity reporting, and financial-crime risk management.
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Identifying major AML data sources including customer records, accounts, transactions, beneficial ownership, risk ratings, screening systems, alerts, investigations, cases, and external risk information.
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Designing AML analytical structures using customer IDs, account IDs, transaction IDs, entity relationships, product classifications, geographic dimensions, risk categories, and reporting periods.
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Establishing AML data-quality principles covering completeness, accuracy, consistency, uniqueness, timeliness, reconciliation, lineage, business definitions, privacy, and controlled access.
Module 2: AML Data Preparation, Integration and Entity Resolution
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Preparing customer and transaction datasets by standardizing names, identifiers, addresses, dates, account references, transaction codes, currencies, countries, and risk classifications.
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Integrating KYC, customer-risk, account, transaction, screening, alert, investigation, and case-management datasets into a consistent analytical environment.
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Developing entity-resolution concepts for identifying relationships among customers, accounts, businesses, beneficial owners, counterparties, addresses, devices, and other relevant entities.
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Using Power Query and analytical data-modeling concepts to clean, transform, combine, and structure AML datasets for repeatable monitoring and reporting workflows. Power BI supports connections to numerous data sources, Power Query transformation, semantic modeling, and interactive analytics.
Module 3: Customer Risk Analytics and AML Segmentation
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Developing customer-risk segmentation frameworks using customer type, occupation, industry, geography, products, channels, ownership structures, transaction behavior, and other relevant risk attributes.
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Analyzing customer risk ratings and changes over time to identify significant behavioral, transactional, geographic, or relationship-level changes requiring additional review.
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Building peer-group analysis that compares customer activity against appropriately defined groups to identify behavior that differs materially from relevant expectations.
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Developing customer-risk dashboards that provide visibility into high-risk populations, risk distributions, geographic exposure, product usage, customer changes, and review status.
Module 4: Transaction Monitoring and Behavioral Analytics
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Analyzing transaction activity using amount, frequency, velocity, timing, counterparties, geography, payment channels, transaction types, and account relationships.
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Developing analytical rules and thresholds for identifying potentially unusual activity while considering customer profiles, expected behavior, peer groups, transaction context, and risk characteristics.
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Applying behavioral-baseline concepts to identify changes in transaction patterns, sudden increases in activity, unusual counterparties, geographic shifts, and other potentially relevant anomalies.
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Measuring transaction-monitoring performance through alert volumes, alert rates, investigation outcomes, escalation patterns, review times, and other operational indicators.
Module 5: AML Typologies, Anomaly Detection and Pattern Analytics
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Understanding how AML typologies can be translated into analytical features, transaction patterns, rules, scenarios, indicators, and investigative hypotheses for data-driven monitoring.
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Applying anomaly-detection concepts to identify unusual transaction values, frequencies, sequences, relationships, locations, timing patterns, and deviations from established behavioral expectations.
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Analyzing structuring, rapid movement of funds, unusual transaction chains, circular flows, dormant-account activity, unusual cross-border patterns, and other illustrative risk scenarios.
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Developing analytical approaches for distinguishing genuine anomalies from legitimate changes in customer activity through segmentation, contextual analysis, investigation, and documented review.
Module 6: Network Analytics and Financial-Transaction Intelligence
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Building transaction networks that represent relationships among customers, accounts, counterparties, businesses, beneficial owners, locations, and financial transactions.
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Applying graph-analysis concepts to identify highly connected entities, clusters, repeated relationships, intermediary patterns, shared attributes, and unusual transaction pathways.
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Investigating relationships across multiple accounts and entities to provide investigators with broader context than isolated transaction-level analysis can provide.
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Developing network intelligence visualizations that allow AML teams to explore relationships, transaction flows, entity connections, and analytical investigation hypotheses.
Module 7: AML Alert, Case and Investigation Analytics
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Analyzing alert volumes, alert types, customer segments, transaction scenarios, dispositions, escalations, false-positive patterns, investigation duration, and investigator workloads.
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Measuring case-management performance using investigation aging, escalation rates, case outcomes, recurring entities, typologies, documentation completeness, and review-cycle indicators.
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Developing analytical approaches for alert prioritization using customer risk, transaction characteristics, behavioral changes, network relationships, historical outcomes, and other relevant indicators.
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Creating investigation dashboards that connect alerts, customers, transactions, cases, investigators, typologies, outcomes, and time-based performance measures for operational intelligence.
Module 8: Predictive AML Analytics, Model Validation and Emerging Risk
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Exploring classification, anomaly detection, clustering, similarity analysis, and other machine-learning concepts applicable to AML monitoring and financial-crime risk analytics.
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Preparing historical AML data for model development by defining target outcomes, selecting analytical features, handling missing information, constructing validation datasets, and evaluating model performance.
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Examining model-validation concepts including precision, recall, investigation outcomes, false-positive patterns, stability, explainability, drift, and ongoing performance monitoring.
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Evaluating responsible use of advanced analytics by incorporating governance, documentation, human oversight, privacy, explainability, and appropriate controls into AML analytical processes.
Module 9: AML Dashboards, KPIs and Regulatory Management Reporting
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Designing AML KPI frameworks covering customer risk, transaction-monitoring activity, alert volumes, investigation performance, case aging, escalation, typologies, geographic exposure, and operational workloads.
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Building Power BI semantic models, calculations, relationships, and analytical measures for repeatable AML reporting and management intelligence.
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Creating interactive AML reports with filters, drill-through analysis, trend views, segmentation, geographic analysis, exception reporting, and investigation-oriented analytical pathways.
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Developing governed AML dashboards using appropriate access controls, refresh processes, alerts, sharing mechanisms, and security features for compliance, risk, audit, and executive stakeholders. Power BI supports scheduled refresh, alerts, sharing, row-level security, sensitivity labels, and audit-related capabilities.
Module 10: Capstone Anti-Money Laundering Data Analytics Project
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Preparing and validating a realistic AML dataset containing customer profiles, accounts, transactions, risk classifications, counterparties, alerts, cases, geographic attributes, and investigative outcomes.
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Developing an integrated AML intelligence dashboard presenting customer-risk distributions, transaction activity, monitoring alerts, case performance, typologies, geographic exposure, and analytical exceptions.
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Conducting a detailed AML investigation using segmentation, behavioral analysis, anomaly detection, transaction patterns, relationship analysis, and network-intelligence concepts.
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Presenting a final AML analytics report that combines data-quality findings, analytical observations, investigation insights, dashboard outputs, model considerations, governance requirements, and an actionable AML analytics improvement roadmap.
Training Approach
This course will be delivered by our skilled trainers who have vast knowledge and experience as expert professionals in the fields. The course is taught in English and through a mix of theory, practical activities, group discussion and case studies. Course manuals and additional training materials will be provided to the participants upon completion of the training.
Tailor-Made Course
This course can also be tailor-made to meet organization requirement. For further inquiries, please contact us on: Email: training@upskilldevelopment.com Tel: +254 721 331 808
Training Venue
The training will be held at our Upskill Training Centre. We also offer training for a group (at a discount of 10% to 50%) at requested location all over the world. The Onsite course fee covers the course tuition, training materials, two break refreshments, buffet lunch, airport transfers, Upskill gift package, and guided tour.
Visa application, travel expenses, dinners, accommodation, insurance, and other personal expenses are catered by the participant
Certification
Participants will be issued with Upskill certificate upon completion of this course.
Airport Pickup and Accommodation
Airport pickup and accommodation is arranged upon request. For booking contact our Training Coordinator through Email: training@upskilldevelopment.com, +254 721 331 808
Terms of Payment:
Unless otherwise agreed between the two parties’ payment of the course fee should be done 3 working days before commencement of the training so as to enable us to prepare better.
