A new report by Forrester reveals that battling fraud is particularly acute in the Asia Pacific region, where the attack rate is above the global average.
The report 'Top Trends Shaping Fraud Management In Asia Pacific, 2024' highlights the role of advanced AI/ML algorithms in combating fraud. These technologies, capable of detecting anomalies and deep fakes, are crucial in the face of evolving threats that exploit institutional vulnerabilities.
Further, fraudsters have been leveraging deep insights for deception, adding to existing techniques like phishing, pretexting, and vishing. Malicious players have been using deep fake technology to access sensitive data and trick victims into transferring money voluntarily.
Fraudsters also exploit APAC's diverse digital payment landscape through SIM swap fraud, QR code scams, and the creation of fake payment apps.
Moreover, fraudsters exploit the prevalence of e-invoicing by counterfeiting electronic invoices and using fake email addresses.
AI and ML-powered fraud management
As malicious players use artificial intelligence and machine learning to deploy fraudulent tactics, financial institutions and merchants also use such technologies.
Forrester finds that GenAI bolsters AI-based fraud model training by creating synthetic data, automating analytical tasks, and generating dynamic risk assessments.
The report also finds that more financial institutions are adopting explainable AI (XAI) for transparency.
Machine Learning also augments efficient link analysis by enabling deeper network link analysis for FIs and merchants. This allows the visualisation of the flow of funds for more insights into the fraud management space.
Technology adoption
Meng Liu, senior analyst at Forrester, said: "Fraud management is becoming more and more challenging for organizations in APAC, yet a clear way to outpace increasingly sophisticated fraud attacks hinges on continued collaboration and key technology adoption."
He recommends organisations embrace new technology, collaborate across teams, and share data across institutions.
