Vericent Insights

Latest insights, research, and thought leadership

Australia's AML/CTF Framework Has Entered a New Era

Australia's AML/CTF reforms are now live. Learn what changed, who is affected, and why risk-based monitoring is becoming essential for regulated businesses.

The Fraud Landscape in 2027: What AI Anomaly Detection Must Do Next to Stay Ahead

Cybercrime will cost $24 trillion by 2027. Agentic AI fraud is already live. Here is what AI anomaly detection must evolve to do, and what the 2026 data tells us about 2027

Model Governance for AI Fraud Systems: Building the Audit Trail That Regulators Now Demand

SR 26-2 replaces SR 11-7 in April 2026. SS1/23 and the EU AI Act follow. Regulators now demand explainable, auditable AI fraud models.

From Rules to Reasoning: How Machine Learning Eliminates the False Positive Crisis in Enterprise Fraud

Rule-based fraud systems generate false positives that cost institutions billions and erode customer trust. Machine learning changes the logic entirely. Here is how.

Privacy-Preserving AI in Fraud Detection: How to Build Models That Regulators and Data Teams Both Accept

GDPR, EU AI Act, and fraud detection AI now converge into one compliance challenge. Learn what regulators require, and how privacy-preserving techniques resolve the tension.

Behavioural Anomaly Detection: How AI Stops Account Takeover Before the Password Is Stolen

Account takeover bypasses passwords entirely. Behavioural anomaly detection flags the attacker before the first fraudulent transaction. See how and why it matters now.

AML Meets AI: Why Fraud Detection and Anti-Money Laundering Are Converging in 2026

156 jurisdictions now classify fraud as a top money laundering risk. Discover why AML and fraud detection are converging in 2026, and how AI is the integration layer.

Real-Time Fraud Monitoring vs Batch Processing: The Architecture Decision That Defines Your Loss Exposure

Real-time fraud monitoring stops fraud before funds leaves. Batch processing detects it after. In 2026, that timing gap costs your organisation, here is the evidence.

Synthetic Identity Fraud in 2026: How Graph Neural Networks Catch What Rule Engines Miss

Synthetic identity fraud is becoming a major financial crime risk in 2026. Learn how Graph Neural Networks detect fraud networks that traditional rule engines miss with data, implementation realities, and governance implications.

The False Positive Problem: Why Legacy Fraud Detection Is Costing You Twice

False positives in fraud detection create hidden operational and customer costs. Learn why AI-driven fraud monitoring is replacing legacy rule-based systems.