Artificial Intelligence in Fraud Detection Training Course

Participants can specify the data needed to support a fraud detection model and its controls.

• Transaction, customer, device and case data mapped to fraud decisions
• Entity identifiers reconciled across payment, CRM, sanctions and case systems
• Data lineage documented from source extraction through model scoring outputs
• Missing values, duplicates and delayed feeds assessed for detection impact
• Label quality tested against confirmed fraud, chargebacks and investigation outcomes
• Retention periods aligned with investigation needs, privacy rules and audit evidence

Participants can construct defensible fraud labels and target variables for model development.

• Confirmed fraud labels distinguished from suspicion, disputes and operational errors
• Target leakage identified between post-event outcomes and scoring-time variables
• Observation windows and outcome windows set for transaction fraud prediction
• Class imbalance measured using fraud prevalence and label distribution checks
• Negative populations sampled without contaminating legitimate customer behaviour patterns
• Typology changes tracked through label definitions and model segmentation

Participants can design fraud features that capture behaviour, relationships and temporal patterns.

• Velocity features calculate transaction counts, values and frequency over time
• Behavioural baselines compare current activity with customer historical patterns
• Peer-group features benchmark customers against comparable accounts and merchants
• Device, location and channel signals identify account takeover indicators
• Aggregation windows prevent information leakage from future transaction activity
• Feature dictionaries record definitions, sources, owners and known limitations

Participants can design a controlled detection approach combining rules and machine-learning scores.

• Rule engines tested for coverage, conflicts, expiry dates and override ownership
• Risk scores combined with threshold rules for controlled alert generation
• Champion-challenger design compares legacy rules against new model performance
• Business constraints encode prohibited actions, mandatory referrals and review requirements
• Decision matrices assign actions by score band, evidence and customer context
• Hybrid architectures preserve known typology controls alongside statistical detection

Participants can select and assess supervised learning methods for labelled fraud populations.

• Logistic regression establishes interpretable baseline fraud classification performance
• Decision trees expose nonlinear fraud indicators and operational decision paths
• Random forests reduce variance across correlated transaction and behaviour features
• Gradient boosting improves ranking performance for complex fraud patterns
• Training, validation and test sets separated using time-aware data splits
• Cross-validation choices account for rare events and temporal fraud drift

Participants can apply anomaly detection methods where confirmed fraud labels are incomplete.

• Isolation Forest identifies unusual transactions through random feature partitioning
• Local Outlier Factor compares observations against neighbourhood density patterns
• Autoencoder reconstruction error flags behaviour poorly represented in training data
• Clustering separates customer behaviour segments before anomaly score comparison
• Anomaly scores calibrated against investigator capacity and available review evidence
• Novel typologies distinguished from seasonal activity, operational changes and data errors

Participants can identify network features and graph patterns associated with organised fraud.

• Graph nodes represent customers, accounts, devices, merchants and beneficiaries
• Edges capture payments, shared devices, addresses and ownership relationships
• Connected components reveal linked accounts requiring coordinated investigation review
• Centrality measures identify influential intermediaries and potential mule coordinators
• Community detection exposes tightly connected clusters with unusual transaction flows
• Network features incorporated into alert scores without obscuring investigator evidence

Participants can define appropriate NLP applications for fraud reports, alerts and investigator notes.

• Text classification routes complaints, claims and alerts by fraud typology
• Named entity recognition extracts people, accounts, merchants and locations
• Topic modelling groups recurring narratives within investigator case notes
• Keyword rules tested against spelling variants, abbreviations and multilingual text
• Retrieval methods locate relevant policies, prior cases and typology guidance
• Sensitive narrative data controlled through access restrictions and redaction rules

Participants can assess controlled generative AI uses within fraud investigation workflows.

• Case summaries generated from approved evidence with mandatory source citations
• Prompt templates constrain outputs to investigation facts and specified formats
• Retrieval-augmented generation grounds responses in controlled policy and case repositories
• Hallucination tests compare generated conclusions against source evidence and reviewer judgement
• Human approval gates prevent unsupported escalations, closures and customer communications
• Prompt logs retained for quality review, auditability and incident investigation

Participants can evaluate fraud models using detection, operational and financial performance measures.

• Confusion matrices separate true positives, false positives and missed fraud
• Precision and recall assessed against alert volumes and fraud detection needs
• F1 score balances precision and recall for comparative model selection
• Precision-recall curves outperform accuracy for highly imbalanced fraud populations
• Thresholds selected using fraud loss, review cost and customer intervention impact
• Calibration testing checks whether predicted probabilities match observed fraud rates

Participants can design an alert workflow that converts model outputs into controlled investigations.

• Alert queues ranked by expected loss, confidence and available evidence
• Reason codes present principal risk indicators to fraud investigators
• Service-level targets distinguish urgent payment intervention from routine review
• Case management fields capture investigator decisions, rationale and evidence sources
• Feedback loops convert investigation outcomes into corrected training labels
• Quality assurance samples closed alerts, escalations and automated decisions

Participants can explain model outputs to investigators, control owners and review functions.

• SHAP values quantify feature contributions to individual fraud risk scores
• Global feature importance identifies drivers across the scored population
• Reason codes translated into plain language without overstating causal conclusions
• Counterfactual testing examines score changes under plausible alternative conditions
• Investigator evidence distinguished from model indicators and statistical associations
• Explanation records retained with alert decisions and subsequent case outcomes

Participants can test fraud models for unequal outcomes and define proportionate mitigations.

• Protected characteristics identified within legal, ethical and data-access constraints
• Disparate impact tests compare intervention rates across relevant customer groups
• False-positive disparities examined for blocked payments and account restrictions
• Proxy variables assessed for indirect discrimination and unjustified correlation
• Fairness trade-offs documented alongside fraud loss and detection performance
• Customer remediation paths address incorrect declines, blocks and adverse actions

Participants can prepare a validation plan for an AI-enabled fraud detection model.

• Model inventories record purpose, owner, version, dependencies and risk rating
• Independent validation challenges assumptions, data, methodology and outcome stability
• Back-testing compares prior scores with realised fraud and investigator outcomes
• Sensitivity analysis tests performance under changed thresholds and input distributions
• Concept drift monitored through population stability and fraud-rate changes
• Validation findings tracked through remediation owners, deadlines and closure evidence

Participants can establish governance controls for lawful, accountable fraud AI deployment.

• FATF risk-based approach connects detection controls with documented financial crime risks
• Data minimisation limits model inputs to defined fraud detection purposes
• Access controls separate data preparation, model development and approval responsibilities
• Human oversight requirements define review authority, escalation and intervention boundaries
• Audit trails preserve inputs, scores, decisions, overrides and model versions
• Third-party governance assesses vendor data use, testing evidence and contractual controls

Participants can plan the deployment of a fraud detection model into operational processes.

• Batch and real-time scoring compared against payment decision timing requirements
• Application programming interfaces define score requests, responses and error handling
• Feature stores control consistent variables between development and production
• Release management documents testing, approvals, rollback triggers and deployment evidence
• RACI matrices assign accountability across fraud, data, technology and compliance
• Operational dashboards track alerts, losses, model health and investigator workload

Participants can assess fraud AI suppliers and compare internal build options objectively.

• Vendor claims tested against labelled data, scenarios and operational acceptance criteria
• Model documentation requested for features, performance, explainability and limitations
• Data residency, retention and subcontractor arrangements assessed before procurement approval
• Integration effort estimated across data feeds, case systems and payment platforms
• Commercial terms reviewed for model updates, support, audit access and liability
• Build-versus-buy scorecards compare control, cost, skills and implementation timelines

Participants can design ongoing monitoring for fraud effectiveness, data stability and control performance.

• Key risk indicators track fraud losses, detection rates and customer interventions
• Population stability index identifies shifts in production feature distributions
• Performance monitoring uses delayed labels, chargebacks and confirmed case outcomes
• Alert ageing reveals bottlenecks, missed service targets and capacity constraints
• Retraining triggers specify material drift, typology change and validation requirements
• Control testing verifies override use, access logs and approval compliance

Participants can build, review and present a complete fraud AI detection proposal with model, governance and implementation components.

• Use-case charter defines typology, decision, scope, objectives and accountable owner
• Data specification maps sources, labels, features, lineage and quality controls
• Detection design combines model method, rules, thresholds and referral pathways
• Performance plan sets validation measures, loss assumptions and acceptance criteria
• Governance pack documents explainability, fairness, privacy, oversight and audit trails
• Implementation roadmap sequences integration, piloting, approvals, monitoring and retraining
• Peer review challenges assumptions, evidence gaps, control weaknesses and delivery risks