Fraud Detection and Prevention

Overview#

A fraud analyst at a payment processor watches a new account pass all standard verification checks on day one, stay quiet for three weeks, then conduct 47 transactions in six hours targeting gift card purchases at a dozen different merchants. By the time the pattern is visible in a manual review queue, the loss is done. The difference between catching that account before the first transaction and reviewing it after the forty-seventh comes down to real-time scoring against hundreds of simultaneous signals, which is what Argus Fraud Detection and Prevention provides.

The platform detects and prevents fraud before it impacts organisations through pattern recognition, anomaly detection, and automated risk scoring across transactions, identities, cards, and insurance claims. Real-time transaction analysis, behavioural anomaly detection, geographic intelligence, velocity checks, and cross-channel monitoring work together to identify and stop fraud in progress.

As fraud techniques evolve, the platform's machine learning models continuously adapt, learning from confirmed fraud cases and analyst feedback to improve detection accuracy while reducing false positive rates that burden investigation teams.

Open Standards#

• OASIS STIX 2.1 / TAXII 2.1: Fraud indicators, threat actor profiles, and attack pattern objects are stored and exchanged as STIX 2.1 Indicator SDOs; TAXII 2.1 feed configurations enable automated polling of external threat intelligence collections for enriching fraud detection.
• MITRE ATT&CK: Attack pattern profiles carry native MITRE ATT&CK technique identifiers (e.g. T1078 Valid Accounts, T1110 Brute Force), allowing fraud tactics such as account takeover and credential stuffing to be classified and cross-referenced against the framework.
• FATF Recommendations / EU AML Directives: Sanctions screening and entity risk profiling are maintained in compliance with FATF 40 Recommendations and the EU Anti-Money Laundering Directives; the platform screens against OFAC SDN, UN, and EU consolidated lists to satisfy these obligations.
• FinCEN SAR (Suspicious Activity Report) format: The financial transaction analysis domain models Suspicious Activity Reports as first-class objects, enabling structured SAR generation and downstream regulatory filing consistent with FinCEN requirements.
• OpenSanctions FollowTheMoney (FtM) data format: Nightly bulk data synchronisation from OpenSanctions.org ingests newline-delimited FtM JSON entity records covering sanctions lists, politically exposed persons, and organised crime datasets used to enrich fraud and identity risk scores.
• PCI DSS (Payment Card Industry Data Security Standard): The compliance framework catalogue includes PCI DSS as a tracked control set, supporting audit documentation and rule coverage mapping for card-present and card-not-present fraud prevention programmes.
• FIDO2 / W3C WebAuthn: Passwordless authentication credentials are registered and verified via the FIDO2/WebAuthn protocol, providing phishing-resistant step-up verification for high-risk transactions and reducing account takeover fraud.
• GraphQL (June 2018 specification): All fraud detection queries, risk score retrieval, case creation, and rule management are exposed through a Strawberry-based GraphQL API, providing a typed, self-documenting interface for integrating clients.

Last Reviewed: 2026-02-23 Last Updated: 2026-04-14

Key Features#

Real-Time Detection#

• High-velocity transaction processing with real-time risk scoring using hundreds of signals
• Velocity checks flagging unusual transaction frequency and volume against established baselines
• Cross-channel detection monitoring across cards, ACH, wire transfers, and digital wallets simultaneously
• Geographic intelligence identifying impossible travel and location-based fraud patterns
• Device fingerprinting and session analysis for digital channel fraud prevention
• Synthetic identity detection identifying fabricated identities from combined real and fictitious information
• Account takeover detection monitoring login patterns, device changes, and credential usage anomalies

Behavioural Analytics#

• Behavioural analysis detecting anomalies in spending patterns, account activity, and user behaviours
• Peer group analysis comparing behaviour against similar customer profiles for contextual risk assessment
• Time-series analysis for temporal fraud patterns revealing cyclical or event-triggered fraud activity
• Account lifecycle monitoring from opening through maturation for early warning of fraudulent accounts
• Customer risk profiling with dynamic risk scores that update based on ongoing behaviour

Pattern and Network Analysis#

• Pattern recognition for known fraud schemes combined with anomaly detection for novel attacks
• Network analysis for coordinated fraud rings identifying connected accounts and shared attributes
• Synthetic identity detection uncovering sophisticated identity fraud schemes using fabricated credentials
• Link analysis connecting seemingly unrelated accounts through shared devices, addresses, or behavioural patterns
• Insurance fraud detection covering staged accidents, provider fraud, and organised fraud rings

Response and Operations#

• Automated alerts with instant notification of high-risk activities to appropriate response teams
• Machine learning models that continuously improve detection accuracy and reduce false positives
• Case creation and investigation workflow for confirmed fraud alerts requiring deeper analysis
• Loss quantification and recovery tracking for financial impact measurement
• Fraud trend reporting and analytics for organisational awareness and strategy development
• Rule management tools enabling fraud analysts to create, test, and deploy detection rules
• False positive management with analyst feedback loops that improve model accuracy
• Regulatory compliance documentation for fraud prevention programme audits and examinations
• Merchant fraud monitoring analysing transaction patterns for suspicious point-of-sale activity
• Cross-channel fraud correlation connecting activity across online, mobile, and in-person channels

Customer Experience Protection#

• Friction-appropriate authentication applying additional verification only when risk indicators warrant it
• Legitimate transaction protection minimising false declines that impact genuine customer activity
• Customer notification tools for suspected fraud alerts and verification requests

Use Cases#

Transaction Fraud Prevention. Monitor credit card, ACH, wire transfer, and digital wallet transactions in real-time, scoring each for fraud risk and automatically blocking or flagging suspicious activity before losses occur. Reduce fraud losses while minimising false declines that impact legitimate customers.

Identity Fraud Detection. Identify synthetic identities, account takeover attempts, and application fraud through behavioural analytics, identity verification, and cross-referencing against known fraud patterns. Detect sophisticated identity schemes that evade traditional verification methods.

Insurance Claims Fraud. Detect staged accidents, inflated claims, and organised fraud rings through claim pattern analysis, provider network mapping, and cross-carrier intelligence. Identify suspicious claims early in the process to prioritise investigation resources.

Internal Fraud Detection. Monitor employee transactions, access patterns, and behavioural anomalies to identify internal fraud, embezzlement, and policy violations. Establish behavioural baselines and detect deviations that may indicate insider threats.

Integration#

• Connects with banking and payment processing systems for real-time transaction monitoring
• Integrates with identity verification and KYC platforms for customer authentication
• Links to case management workflows for investigation, documentation, and reporting
• Works with insurance claims systems for automated fraud screening and referral
• Supports regulatory reporting for suspicious activity filings and compliance documentation
• Compatible with law enforcement systems for fraud case referral and intelligence sharing
• Feeds into enterprise risk dashboards for organisational fraud exposure visibility
• Chargeback analysis and dispute management for transaction fraud recovery coordination
• Third-party vendor and partner fraud monitoring for supply chain risk management