Static vs. Dynamic eCommerce Antifraud Tools
Would you like to see a 2-6% increase in sales and up to 98% fewer chargebacks?
That’s the dramatic business impact you could see with dynamic fraud detection 2.0 tools and techniques instead of “old school” static methods, according to the Mercator Advisory Group, the leading independent research and advisory services firm exclusively focused on the payments and banking industries.
The disadvantages of static?
Fraudsters using bots and other advanced techniques are quickly adapting to static fraud detection methods. As soon as merchants identify phony transactions, fraudsters simply change their angle of attack and quickly spoof static fraud defenses—by using different devices, addresses, or card numbers. Online businesses—increasingly unsure of the accuracy of their risk assessments—face the triple-edged sword of higher chargebacks AND decreased sales AND degraded customer service. Here’s why:
Static fraud prevention measures not only run the risk of allowing fake merchandise orders to get approved, but they also are prone to “false positive” decisions—incorrectly flagging legitimate orders as fraudulent. False positives result in lost sales and lost customers. They also have spillover effects, including time-intensive manual reviews. These cause delays in order fulfillment which makes shoppers unhappy.
- Reactive—fighting the last war
- Easily deceived by fraudster spoofing identity
- Generates more false positives
- Requires increased human intervention
- Time intensive—impacts customer order completion
What does static look like?
The list below provides an overview of the static fraud prevention tools that are in use today:
- Internet protocol (IP) and device lookups
- Address verification (AVS)
- Card verification value (CVV)
- Historical databases
- Points scoring or fraud risk scoring systems
- Mobile card controls
- Data mining purchase transaction details or buyer characteristics
The advantages of dynamic?
Dynamic fraud detection 2.0 uses machine learning and other technologies, including order linking and Personas, to distinguish between authentic and fraudulent shoppers as well as to target organized criminal groups that conceal their true identities in fraud attempts. Fraud detection 2.0 makes it virtually impossible for fraudsters to circumvent fraud detection. The result is dramatically more accurate risk assessment, which drives down chargebacks, boosts sales by avoiding false positives, and significantly reduces the need for human intervention and manual reviews.
- Continuous learning and real-time updating
- Fewer manual reviews
- Reduced number of false positives
- Customized—deters fraudster spoofing and mimicking
- More sales and greater customer satisfaction
What does dynamic look like?
- Machine learning and artificial intelligence (AI). These interrelated technologies capitalize on the immense power of today’s computing and memory resources to crunch massive data sets in real-time. Fraud detection 2.0 solutions employing AI and Machine Learning are able to review data, discern relationships that exist in the data, and determine actions to take based on ever-improving detection of rapidly metastasizing fraud attack signatures.
- Dynamic risk scoring. Algorithms continually update based on each user’s ever-changing profile and the merchant’s continually-evolving risk tolerance. Every transaction reveals new information to better predict previously unknown fraud types.
- Persona linking. Statistical mathematical plotting algorithms are used to construct dynamic links between hundreds of variables. The resulting Persona “shortcuts” are then able to identify fraud and fraudsters more rapidly and accurately.
- Mobile device ID. Mobile networks and devices make it easy for fraudsters to instantly morph identities. Perhaps that’s why 60% of all fraud originates on mobile. Fraud detection 2.0 solutions deploy next-generation mobile device ID technology that dynamically detects anomalies and pinpoints device attributes critical for assessing risk.
- Other dynamic tools and techniques include persistent identity, behavioral biometrics and more.
Are your “old school” fraud detection and antifraud methods driving up costs while harming revenues due to too many false positives? Discover how fraud detection 2.0 is the next-generation method for helping online businesses increase revenue, reduce fraud, and improve business performance.
Read more about how you can get ahead of the curve. Download the white paper “Fraud Detection 2.0: Dynamic Tools For Fighting E-Commerce Fraud".