Guide to Detecting and Foster Prevention of Payment Fraud in 2024
In the rapidly evolving digital landscape, the issue of payment fraud has become a significant concern for businesses and individuals alike. This article explores the various types of payment fraud, its impact, and the strategies for prevention and detection.
Payment fraud, also known as friendly fraud, occurs when someone intentionally makes a purchase online and then falsely claims the transaction was invalid. Other methods include social engineering, phishing, business email compromise (BEC), and enumeration. Enumeration is a technique where hackers determine login credentials using brute-forcing software.
Online gaming scams can trick players into paying small fees, enabling fraudsters to extract larger amounts from the card linked to their Apple ID. A more sophisticated form of fraud, triangulation fraud, involves three parties: an unaware customer, an online shop, and a fraudster as a middleman, often happening through legit marketplaces like Amazon.
The most common method of social engineering is phishing, which uses emails, phone calls, texts, and social media to gather sensitive personal data. This data can then be used to commit various types of payment fraud, such as credit card fraud.
Payment fraud is expected to cause enormous losses, with merchant losses from online payment fraud exceeding $362 billion globally between 2023 to 2028, and $91 billion in 2028 alone, according to Juniper Research. Fraudulent transactions using payment cards alone are expected to increase to $38.5 billion by 2027.
To combat this growing threat, a multilayered approach is essential. This includes real-time transaction monitoring, machine learning (ML), AI-driven behavior analysis, identity verification, and pattern recognition.
Real-time Transaction Monitoring continuously analyses transactions as they occur to detect anomalies based on time, amount, location, and user behavior, enabling immediate fraud alerts and intervention. Machine Learning and AI Models use supervised, unsupervised, and hybrid ML models to detect suspicious patterns dynamically instead of relying on static rules.
Behavioral Analytics monitors user and device behavior, including mouse movements, keystroke dynamics, and device fingerprinting, to detect deviations from normal patterns that suggest fraud. Identity Verification and Biometrics implement robust identity proofing via biometrics, liveness detection, and document verification to prevent identity theft and account takeover fraud.
Risk Scoring and Dynamic Rules assign customizable risk scores to transactions for prioritizing investigations and setting adaptive thresholds to catch emerging fraud patterns without excessive false positives. Sanctions and Blacklist Screening screen transactions against fraudster lists, politically exposed persons (PEPs), and sanctions databases to block high-risk entities immediately.
Industry-Specific Measures address the unique challenges faced by different sectors. For e-commerce, address validation and chargeback fraud detection models mitigate fraud that costs billions annually. Telecom combats SIM swap and identity alteration fraud, while crypto networks use wallet screening for smart contract misuse.
Finally, the integration of tools, such as AI, ML, transaction monitoring, and identity tools, into a cohesive fraud prevention ecosystem enhances detection and prevention capabilities.
Continuous updates to models and frameworks are necessary to address evolving fraud tactics, handle data imbalance, and enable scalable, real-time detection for sustained effectiveness.
As the digital world continues to evolve, so too does the threat of payment fraud. However, by staying informed, protecting personal data, and leveraging advanced technologies, we can effectively combat this growing issue. The WTF Summit 2025 will share insights on the future of fraud and resilience strategies.
Sports, like online gaming, can be vulnerable to payment fraud. For instance, online gaming scams might trick players into paying small fees, allowing fraudsters to extract larger amounts from their linked accounts. To secure transactions in sports and other digital platforms, a multilayered approach is essential, incorporating real-time transaction monitoring, machine learning, behavioral analytics, identity verification, and pattern recognition.
