Digital Finance Risk and Fraud Management Training Course

Participants can classify major digital fraud typologies and identify their warning signs.

• Account takeover through credential stuffing, phishing, malware and social engineering
• Authorised push-payment fraud using impersonation, investment and invoice deception
• Synthetic identity fraud combining fabricated attributes with genuine personal data
• First-party fraud involving deliberate misrepresentation, chargebacks and repayment avoidance
• Merchant fraud patterns including collusion, refund abuse and transaction laundering
• Mule-account networks moving proceeds through layered payment transactions

Participants can produce a scored fraud risk assessment for a digital finance process.

• Inherent-risk scoring using likelihood, impact, velocity and control dependency
• Risk events defined through cause, event and consequence statements
• Assessing customer, product, channel, geography and transaction risk factors
• Control-effectiveness ratings based on design, operation and evidence quality
• Residual-risk calculations supporting treatment priorities and management decisions
• Risk registers linking owners, actions, deadlines and assurance evidence

Participants can map fraud attack paths across a digital customer journey.

• Customer journey mapping from registration through servicing, payments and closure
• Attack-path analysis identifying entry points, weaknesses and fraud objectives
• Mapping data flows between apps, identity providers and payment processors
• Trust boundaries separating customers, employees, vendors and external networks
• Misuse cases documenting fraudulent actions against legitimate service functions
• Control placement decisions at onboarding, authentication and transaction stages

Participants can select proportionate identity controls for digital customer onboarding.

• Know-your-customer evidence requirements for individuals, businesses and beneficial owners
• Document verification checks for authenticity, alteration and expiration indicators
• Liveness detection methods addressing presentation attacks and deepfake impersonation
• Device fingerprinting signals supporting identity confidence and duplicate detection
• Risk-based onboarding routes using customer profile and product-risk criteria
• Manual-review triggers for exceptions, mismatches and high-risk customer attributes

Participants can evaluate controls that reduce account takeover and unauthorised access.

• Multi-factor authentication using knowledge, possession and inherence factors
• Step-up authentication triggered by risky actions and anomalous behaviour
• Credential-stuffing defences using rate limits, bot detection and breached-password checks
• Session management controls for timeout, token renewal and device binding
• Privileged-access controls separating administration, approval and customer-support functions
• Account-recovery procedures resistant to social engineering and identity manipulation

Participants can design controls for common digital payment fraud scenarios.

• Payment-flow mapping across payer, payee, acquirer, issuer and scheme participants
• Velocity controls limiting transaction frequency, value and beneficiary additions
• Confirmation-of-payee checks reducing misdirected and impersonation payment losses
• Beneficiary risk scoring using account age, network links and payment behaviour
• Card-not-present controls using EMV 3-D Secure authentication data
• Chargeback management using reason codes, evidence standards and dispute timelines

Participants can evaluate analytical techniques used to detect suspicious activity.

• Rules-based detection using thresholds, velocity patterns and behavioural deviations
• Supervised machine-learning models trained on labelled fraud outcomes
• Unsupervised anomaly detection identifying unusual transactions without fraud labels
• Feature engineering from transaction, device, behavioural and network data
• Precision, recall, false-positive rate and detection-rate performance measures
• Model-risk controls covering validation, drift monitoring and human review

Participants can document risk-based monitoring scenarios and escalation criteria.

• Scenario objectives linked to typologies, risks and regulatory obligations
• Threshold calibration using transaction populations, peer groups and historical losses
• Behavioural scenarios detecting rapid movement, structuring and unusual counterparties
• Alert segmentation by customer risk, product type and transaction value
• Disposition codes distinguishing false positives, fraud, suspicion and operational errors
• Scenario documentation recording rationale, data sources, owners and review frequency

Participants can identify where fraud controls and financial-crime controls must connect.

• FATF risk-based approach applied to digital products and customer channels
• Money-laundering stages following fraud proceeds through placement and layering
• Sanctions screening of customers, beneficiaries, merchants and payment messages
• Adverse-media review supporting enhanced due diligence and investigation decisions
• Suspicious activity reports supported by clear facts, chronology and rationale
• Information-sharing protocols between fraud, AML, legal and investigations teams

Participants can detect and investigate indicators of mule-account activity.

• Mule recruitment methods involving employment scams, romance fraud and social media
• Inbound funnel-account patterns showing many unrelated credits and rapid withdrawals
• Network analysis linking shared devices, beneficiaries, addresses and identifiers
• Transaction-chain reconstruction tracing funds across accounts and payment instruments
• Mule-risk indicators from account age, turnover and counterparty concentration
• Account restriction decisions balancing evidence, customer impact and legal requirements

Participants can investigate a fraud alert and document an auditable case file.

• Alert triage using risk severity, financial exposure and customer-vulnerability criteria
• Investigation plans defining allegations, evidence sources and decision questions
• Evidence logs preserving source, date, analyst actions and chain custody
• Transaction reconstruction using timestamps, channels, devices and counterparties
• Customer-contact records documenting disclosures, consent and remediation communications
• Case conclusions stating facts, rationale, actions and escalation decisions

Participants can connect cybersecurity events to fraud scenarios and control actions.

• Phishing, malware, SIM swapping and remote-access scam attack mechanisms
• Data-breach impacts on identity theft, account takeover and impersonation fraud
• NIST Cybersecurity Framework functions applied to fraud-control coordination
• Security-event correlation with fraud alerts, customer reports and transaction anomalies
• Data-minimisation principles for fraud analytics and investigation records
• Third-party incident notifications, containment duties and evidence preservation

Participants can assess fraud risks introduced by suppliers, agents and platform partners.

• Third-party due diligence covering ownership, controls, incidents and financial viability
• Outsourced-service risk mapping for onboarding, payments, cloud and customer support
• Contractual control requirements for data access, monitoring and incident notification
• Service-level indicators measuring fraud losses, alert quality and response times
• Agent-network controls addressing identity verification, cash handling and collusion
• Exit plans protecting customer data, transaction records and operational continuity

Participants can build and test a fraud control matrix for a defined process.

• Preventive controls stopping unauthorised activity before financial loss occurs
• Detective controls identifying suspicious events after processing or customer reports
• Corrective controls containing losses, restoring access and improving root causes
• Fraud control matrices linking risks, controls, owners, evidence and testing
• Design-effectiveness testing against stated objectives, triggers and failure modes
• Operating-effectiveness testing using samples, walkthroughs and retained evidence

Participants can coordinate a fraud incident response and customer remediation plan.

• Incident classification based on loss severity, customer impact and regulatory significance
• Containment actions including account freezes, payment recalls and credential resets
• Crisis roles using incident commander, investigators, legal and communications leads
• Customer-vulnerability assessment for scam victims and hardship considerations
• Root-cause analysis using five whys and fault-tree methods
• Post-incident actions tracking control fixes, owners, evidence and closure dates

Participants can report fraud losses, control performance and residual risk to management.

• Gross-loss measurement including principal loss, recovery costs and customer reimbursement
• Net-loss calculations reflecting recoveries, chargebacks, insurance and provisions
• Fraud-rate metrics by transaction value, volume, customer and channel
• Key risk indicators for alerts, losses, control failures and emerging typologies
• Management information packs showing trends, thresholds, root causes and actions
• Board reporting distinguishing operational detail from strategic risk decisions

Participants can establish governance arrangements for accountable fraud risk management.

• Fraud policy components covering scope, roles, reporting and investigation standards
• Risk committee mandates for challenge, escalation and treatment decisions
• ISO 31000 principles applied to fraud risk governance and accountability
• Control attestations supported by evidence, exceptions and remediation commitments
• Regulatory examination preparation through traceable records and documented decisions
• Training and awareness programmes targeted by role, threat and control responsibility

Participants can embed fraud requirements into digital product and change delivery.

• Fraud requirements defined during product discovery and customer-journey design
• Threat modelling using assets, actors, attack paths and mitigations
• Change-risk assessments covering new features, integrations and payment limits
• Pre-release testing for authentication, monitoring, alerts and exception handling
• Control acceptance criteria agreed by product, risk, compliance and operations
• Post-launch monitoring using early-warning indicators and customer complaint data

Participants can build, review and present a complete fraud-management plan for a digital finance product or process.

• Define product scope, customer segments, transactions and critical third parties
• Assess inherent risks using documented fraud typologies and attack paths
• Construct a control matrix covering prevention, detection, investigation and recovery
• Specify monitoring scenarios, thresholds, alert workflows and escalation criteria
• Develop incident-response, customer-remediation and regulatory-reporting procedures
• Present residual-risk decisions, implementation priorities and management-information measures