Advanced Fraud Analytics and Data Mining Course

Name: Advanced Fraud Analytics and Data Mining Training Course
Availability: InStock

Build defensible fraud models, network insights, alert priorities, and investigation handoffs from governed data.

Advanced Fraud Analytics and Data Mining Course

At a glance

Duration: 2 Weeks
Format: Classroom
Cities: Madrid, Montreux, Prague, London, Baku, Bangkok and more
Next session: 19 – 30 October 2026, Madrid
Average fee: 9,900 €

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Advanced Fraud Analytics and Data Mining Training Course

Overview

Advanced Fraud Analytics and Data Mining Training Course is a two-week advanced course for fraud, risk, audit, compliance, and analytics professionals who leave with a Fraud Analytics Mining Playbook. Participants apply fraud analytics data mining, suspicious pattern detection, anomaly detection, fraud network analysis, and alert triage to convert fragmented signals into governed investigative decisions. The course connects model evidence, operational controls, and investigation handoffs. Agile Leaders Training Center develops advanced fraud analytics capability.

Who Should Attend

Fraud analysis functions responsible for detecting and validating suspicious transaction and entity behavior
Financial crime investigation functions responsible for turning analytical alerts into documented case leads
Risk analytics functions responsible for building, evaluating, and monitoring fraud indicators
Internal audit functions responsible for testing fraud risks, controls, and anomalous activity
Compliance data functions responsible for controlled alert review and evidence handoffs
Data specialists responsible for preparing features, labels, networks, and validation samples

The course assumes participants can analyze structured data, interpret model outputs, and document fraud-risk decisions, and leaves out introductory statistics, production software engineering, cybersecurity response, and legal certification preparation.

Departments and Industries

The course supports departments and industries that use analytical evidence to identify, prioritize, and investigate possible fraud.

Fraud management, financial crime, compliance, internal audit, and enterprise risk departments
Banking, payments, insurance, lending, and financial technology operations
Retail, e-commerce, telecommunications, and digital-platform businesses
Procurement, supply chain, manufacturing, and asset-intensive operations
Healthcare claims, public services, and professional-service organizations

Learning Objectives

By the end of this course, participants will be able to:

Apply fraud-risk hypotheses to analytical test design
Build governed features, labels, samples, and network data
Compare supervised, unsupervised, anomaly, and graph methods
Evaluate imbalanced models, thresholds, explanations, and alert utility
Prioritize alerts and build controlled investigation handoffs
Build a monitored Fraud Analytics Mining Playbook

Course Agenda

Day 1: Frame Fraud Analytics Decisions

Fraud Risk Hypothesis Canvas
Fraud Scheme and Red-Flag Taxonomy
Analytical Use-Case Prioritization Matrix
Decision Owner and Stakeholder Map
Fraud Analytics Control Boundary

Day 2: Prepare Governed Fraud Data

Transaction and Entity Data Inventory
Data Lineage and Permitted-Use Record
Missingness and Data Quality Profile
Identity Resolution and Entity Matching Rules
Fraud Data Leakage Review Checklist

Day 3: Engineer Fraud Signals

Behavioral Velocity Feature Catalogue
Peer-Group Deviation Feature Set
Temporal Sequence and Event Window Design
Label Definition and Case Outcome Register
Training, Validation, and Holdout Sample Plan

Day 4: Build Supervised Detection Models

Logistic Regression Fraud Scorecard
Decision Tree and Random Forest Comparison
Gradient-Boosting Classification Workflow
Cost-Sensitive Learning Decision Table
Probability Calibration and Score Mapping

Day 5: Detect Unknown Fraud Patterns

Statistical Outlier Baseline Profile
Isolation Forest Anomaly Detection Method
Clustering-Based Peer Segmentation
Density and Distance Threshold Review
Novel Pattern Investigation Queue

Day 6: Analyze Fraud Networks

Entity-Relationship Graph Data Model
Shared-Identifier Link Analysis
Connected-Component Detection Method
Community and Cycle Pattern Analysis
Centrality and Suspicious Hub Scorecard

Day 7: Evaluate Detection Performance

Class-Imbalance Evaluation Framework
Precision, Recall, and Ranking Evidence Table
False-Positive Cost and Capacity Model
Threshold Optimization Decision Curve
Champion-Challenger Test Record

Day 8: Explain and Prioritize Alerts

Model Explanation Evidence Pack
Reason-Code Design and Validation
Alert Risk and Urgency Prioritization Matrix
Investigator Capacity Allocation Board
Customer Friction and Control Tradeoff Log

Day 9: Govern Investigation Handoffs

Alert-to-Case Handoff Checklist
Analytical Evidence and Limitation Record
Case Feedback and Label Correction Loop
Model Drift and Fraud Change Indicators
Monitoring, Escalation, and Review Calendar

Day 10: Practice Fraud Analytics Decisions

Exercise: Convert a Fraud Risk into Analytical Tests
Exercise: Build Features and Select Detection Methods
Exercise: Evaluate an Imbalanced Fraud Model
Exercise: Prioritize Alerts and Prepare a Case Handoff
Capstone: Fraud Analytics Mining Playbook

Practical Exercises

The course uses suggested activities based on payment, claims, procurement, and digital-commerce scenarios.

Suggested activity: convert a fraud-risk hypothesis into red flags, data requirements, features, labels, and analytical tests.
Suggested activity: compare classification, anomaly, and graph methods against evidence quality and investigation needs.
Suggested activity: select thresholds using detection value, false positives, capacity, and customer-friction evidence.
Suggested activity: assemble explanations, alert priorities, case handoffs, feedback controls, and monitoring indicators.

FAQs

Who suits advanced fraud analytics training, and what does it assume?

Fraud, investigation, risk, audit, compliance, and data functions suit the training; it assumes participants can analyze structured data, interpret model evidence, and document operational decisions.

How does fraud analytics data mining differ from general data mining strategy?

Fraud analytics data mining concentrates on rare-event labels, evolving behavior, suspicious networks, investigation capacity, evidence handoffs, and monitoring, while general data mining strategy addresses broader pattern-discovery decisions.

How should imbalanced fraud detection models be evaluated?

Imbalanced fraud detection models should be evaluated through ranking quality, precision, recall, calibration, false-positive cost, investigation capacity, segment stability, threshold effects, and performance on untouched validation and holdout samples.

How does fraud network analysis improve suspicious pattern detection?

Fraud network analysis reveals shared identifiers, connected groups, cycles, hubs, indirect links, and coordinated behavior that transaction-by-transaction scoring can miss.

How should fraud alert triage support investigations?

Fraud alert triage should combine risk, urgency, explanation quality, potential impact, available evidence, duplication checks, and investigator capacity into a documented prioritization and handoff process.

Conclusion

Participants take back a Fraud Analytics Mining Playbook containing hypotheses, data controls, features, method choices, validation evidence, thresholds, explanations, triage rules, investigation handoffs, and monitoring indicators. It changes disconnected analytical tests into a governed detection-to-investigation process. The playbook connects fraud-risk decisions with measurable evidence, operational ownership, and feedback.

credits: 5 credit per day

Course Mode: full-time

Provider: Agile Leaders Training Center

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Showing 21-40 of 74 events

Location	Dates	Duration	Mode	Price
Madrid	Week 02, 2027 11 – 22 January 2027	2 Weeks	Onsite	€10,000
Toronto	Week 02, 2027 17 – 28 January 2027	2 Weeks	Onsite	€16,000
Munich	Week 05, 2027 1 – 12 February 2027	2 Weeks	Onsite	€10,000
Dubai	Week 06, 2027 8 – 19 February 2027	2 Weeks	Onsite	€8,500
Amsterdam	Week 06, 2027 8 – 19 February 2027	2 Weeks	Onsite	€10,000
San Diego	Week 06, 2027 8 – 19 February 2027	2 Weeks	Onsite	€28,000
Nairobi	Week 06, 2027 14 – 25 February 2027	2 Weeks	Onsite	€6,400
Casablanca	Week 07, 2027 15 – 26 February 2027	2 Weeks	Onsite	€7,000
Kuala Lumpur	Week 07, 2027 15 – 26 February 2027	2 Weeks	Onsite	€9,000
Zoom	Week 08, 2027 22 February – 5 March 2027	2 Weeks	Online	€3,000
Berlin	Week 08, 2027 22 February – 5 March 2027	2 Weeks	Onsite	€10,000
Manama	Week 08, 2027 28 February – 11 March 2027	2 Weeks	Onsite	€8,000
Cairo	Week 09, 2027 1 – 12 March 2027	2 Weeks	Onsite	€7,000
Amman	Week 09, 2027 7 – 18 March 2027	2 Weeks	Onsite	€7,000
Tashkent	Week 09, 2027 7 – 18 March 2027	2 Weeks	Onsite	€9,000
Accra	Week 10, 2027 14 – 25 March 2027	2 Weeks	Onsite	€5,800
Jakarta	Week 11, 2027 15 – 26 March 2027	2 Weeks	Onsite	€10,000
London	Week 12, 2027 22 March – 2 April 2027	2 Weeks	Onsite	€10,000
Abu Dhabi	Week 12, 2027 22 March – 2 April 2027	2 Weeks	Onsite	€8,000
Tokyo	Week 13, 2027 29 March – 9 April 2027	2 Weeks	Onsite	€15,000

Frequently asked questions

What does this course cover?

OverviewAdvanced Fraud Analytics and Data Mining Training Course is a two-week advanced course for fraud, risk, audit, compliance, and analytics professionals who leave with a Fraud Analytics Mining Playbook. Participants apply fraud analytics data mining, suspicious pattern detection, anomaly detection, fraud network analysis, and alert triage to convert…

Are training dates available?

Yes. Available dates and destinations are listed in the course dates section on this page.

How can I register?

Choose an available date on this page and complete the registration form, or send a programme enquiry.

Can I download the course brochure?

Yes. Use the brochure download link provided on this page.

Advanced Fraud Analytics and Data Mining Course