Fraud Analytics Training Course

Participants can identify fraud typologies and define indicators suitable for data testing.

• Procurement fraud indicators: bid rotation, vendor collusion and invoice inflation
• Payroll schemes involving ghost employees, overtime abuse and bank-account duplication
• Expense claim patterns including duplicate receipts and policy-threshold splitting
• Payment fraud signals: beneficiary changes, urgent requests and unusual approvals
• Revenue manipulation indicators involving cut-off, round numbers and manual journals
• Account takeover patterns using device changes, credential resets and payee additions

Participants can convert fraud risk assessments into testable analytical hypotheses.

• Process maps locating fraud opportunities at approval, payment and reconciliation points
• Inherent-risk scoring using likelihood, impact, velocity and control vulnerability
• Control design assessment against preventive, detective and corrective objectives
• Risk-and-control matrices connecting schemes, data fields and detection methods
• Hypothesis statements specifying population, indicator, threshold and expected evidence
• Prioritisation criteria based on loss exposure, volume, urgency and investigability

Participants can specify fraud analytics data requirements and assess data fitness.

• Source-system inventory covering ERP, payments, HR, CRM and case-management records
• Data dictionaries defining fields, formats, owners and permitted values
• Completeness tests identifying missing identifiers, dates, amounts and approval records
• Validity checks comparing codes, dates and amounts with business rules
• Entity resolution challenges caused by aliases, transliteration and inconsistent addresses
• Data lineage records linking analytical outputs to source extracts and transformations

Participants can prepare analytical datasets and resolve duplicate entities systematically.

• Standardising names, addresses, telephone numbers and bank-account formats
• Parsing transaction narratives into counterparties, references and payment descriptions
• Deduplication rules using exact matches, fuzzy matches and survivorship criteria
• Record linkage scores combining name, address, account and identifier similarity
• Date normalisation for posting dates, invoice dates and approval timestamps
• Audit trails documenting transformations, exclusions, joins and derived fields

Participants can write SQL queries that test common fraud indicators in transaction data.

• SELECT, WHERE and ORDER BY queries for targeted transaction populations
• JOIN logic connecting invoices, vendors, approvers, employees and payments
• GROUP BY tests identifying repeated amounts, duplicate invoices and shared accounts
• Window functions detecting sequential payments, repeated approvals and threshold splitting
• CASE expressions assigning risk flags from defined fraud indicators
• Common table expressions structuring readable multi-stage fraud detection queries

Participants can use descriptive analysis to identify patterns requiring further review.

• Frequency distributions revealing uncommon payment values, users and transaction types
• Trend analysis comparing monthly volumes, values and exception rates
• Cross-tabulations testing approval patterns by vendor, location and cost centre
• Pareto analysis concentrating review on highest-value suppliers and claimants
• Control charts identifying unusual movements outside expected operating ranges
• Drill-down paths from portfolio metrics to individual transactions and documents

Participants can apply statistical tests appropriately and interpret their limitations.

• Benford's Law testing first-digit and second-digit distributions in financial data
• Z-scores identifying extreme values within defined transaction populations
• Interquartile range method flagging outliers without normal-distribution assumptions
• Coefficient of variation comparing volatility across vendors, branches and accounts
• Peer-group analysis establishing comparable behaviour for anomaly identification
• Statistical significance interpreted alongside materiality, context and evidence quality

Participants can design transparent fraud detection rules and document their rationale.

• Rule logic combining triggering events, exclusions and escalation conditions
• Threshold calibration using historical losses, alert volumes and investigative capacity
• Velocity rules detecting rapid payment sequences and repeated account changes
• Split-transaction rules identifying purchases divided below approval thresholds
• Duplicate-payment rules matching invoice numbers, amounts, dates and vendors
• Rule documentation recording purpose, owner, data fields and review frequency

Participants can rank fraud alerts using risk, evidence and operational criteria.

• Risk scores combining financial exposure, typology severity and customer vulnerability
• Weighted scoring models assigning points to evidence-based fraud indicators
• Alert queues segmented by urgency, value, confidence and regulatory deadlines
• Triage decisions distinguishing closure, monitoring, information requests and investigation
• Service-level targets for initial review, escalation and case closure
• Disposition codes supporting reliable reporting, feedback and model improvement

Participants can identify relationships that indicate collusion, concealment or concentration risk.

• Network nodes representing people, accounts, devices, vendors and addresses
• Relationship edges defined by payments, ownership, approvals and shared identifiers
• Degree centrality highlighting unusually connected entities within transaction networks
• Community detection identifying clusters of counterparties with concentrated interactions
• Circular payment patterns suggesting layering, recycling or invoice round-tripping
• Link charts documenting relationship evidence for investigative review

Participants can test procurement and accounts payable data for fraud indicators.

• Vendor-master tests for duplicate addresses, accounts, tax identifiers and contacts
• Three-way-match exceptions linking purchase orders, receipts and supplier invoices
• Bid-analysis tests identifying rotation, identical pricing and suspicious timing
• Invoice sequencing patterns indicating duplicate billing or fabricated documents
• Approval-path analysis identifying overrides, self-approval and segregation breaches
• Supplier concentration metrics highlighting dependency, conflicts and collusion risks

Participants can identify employee-related fraud indicators in payroll and expense data.

• Ghost-employee tests matching payroll records against HR and identity data
• Shared bank-account analysis identifying employees paid through common accounts
• Overtime anomaly tests comparing schedules, attendance and supervisor approvals
• Expense duplicate detection using merchant, amount, date and receipt references
• Policy-limit clustering identifying claims consistently just below reimbursement thresholds
• Travel-and-expense analysis matching itineraries, bookings, claims and calendar records

Participants can analyse payment and customer behaviour for fraud and account abuse signals.

• Beneficiary-change monitoring linking payee amendments to subsequent payment activity
• Transaction velocity analysis measuring frequency, amount and destination changes
• Device and IP indicators supporting account takeover and mule-account detection
• Customer-behaviour baselines comparing current activity with historical patterns
• Card-not-present indicators including unusual merchants, geographies and authentication failures
• Step-up review triggers for high-risk transfers and unusual customer actions

Participants can distinguish fraud analytics from AML monitoring while identifying overlaps.

• Structuring indicators involving repeated cash transactions below reporting thresholds
• Rapid movement of funds through accounts with limited commercial rationale
• High-risk jurisdiction indicators assessed against customer profile and transaction purpose
• Sanctions-screening alerts reviewed using identifiers, aliases and ownership information
• Source-of-funds analysis linking transaction activity to known customer income
• Suspicious activity narratives separating facts, indicators, analysis and rationale

Participants can assess when anomaly detection and machine learning are suitable for fraud work.

• Supervised learning labels derived from confirmed cases and reliable dispositions
• Unsupervised clustering identifying unusual groups without prior fraud labels
• Isolation Forest concepts for detecting rare multivariable transaction patterns
• Feature engineering using recency, frequency, monetary value and relationship indicators
• Class imbalance addressed through sampling, weighting and threshold selection
• Explainability requirements for analyst review, challenge and investigation handover

Participants can evaluate fraud analytics performance and recommend rule or model changes.

• Confusion matrices separating true positives, false positives and missed cases
• Precision and recall measures aligned with investigation capacity and risk appetite
• Receiver operating characteristic curves comparing detection thresholds and trade-offs
• Back-testing rules against historical cases, losses and confirmed non-fraud outcomes
• Population stability index monitoring shifts in input-data distributions
• Change-control procedures approving rule updates, model releases and retirement

Participants can convert analytical alerts into well-documented investigation case records.

• Case plans defining allegations, scope, evidence sources and investigation objectives
• Evidence logs recording source, custodian, collection date and chain of custody
• Transaction chronologies reconstructing events, approvals, communications and fund movements
• Interview preparation based on corroborated facts rather than analytical assumptions
• Case narratives distinguishing allegations, findings, limitations and recommended actions
• Escalation protocols for legal counsel, HR, compliance and law enforcement

Participants can apply governance controls to fraud analytics data, decisions and reporting.

• Data-minimisation principles limiting fields to defined fraud detection purposes
• Access controls protecting sensitive customer, employee and investigation information
• Retention schedules for alerts, cases, evidence and analytical outputs
• Bias testing examining disparate alert outcomes across customer or employee groups
• Human-review controls for material decisions based on automated risk scores
• Governance reporting covering performance, exceptions, ownership and remediation actions

Participants can build, review and present a complete fraud analytics case file from risk hypothesis through recommended action.

• Risk hypothesis defining scheme, exposure, population and expected indicators
• Data requirements and quality checks supporting reliable analytical conclusions
• Detection logic combining rules, statistics, relationships and alert prioritisation
• Alert review record documenting evidence, limitations, disposition and escalation rationale
• Management report presenting findings, control weaknesses, losses and remediation priorities
• Peer review against accuracy, explainability, evidence quality and governance requirements