Online fraud mitigation technology analyzes traffic using device identifiers and behavioral patterns, and uses risk scores to determine whether the activity is trusted, fraudulent or requires further review – either through manual analysis or step-up authentication.
In the 2020 Gartner Cool Vendors in IAM and Fraud Prevention report, the analyst firm highlights the fact that mitigation-focused controls, which rely on threat scoring and behavioral analysis, cannot stop fraud and automated abuse within the current threat landscape.
The breakneck speed of digitization is opening up new attack vectors every single day. Fraudsters are manipulating the situation for their own financial gain. They use sophisticated technology and commoditized tools to launch complex attacks that cause financial and reputational losses to businesses.
Fraudsters steal business and consumer data in order to fuel numerous attacks. Account takeover, new fake account creation, payment fraud, fake reviews, spam, API abuse are some of the common ways to attack businesses. Further, to scale the attacks and improve their return on investment, fraudsters rely heavily on automation and low-cost human sweatshops.
Data breaches, API attacks, and scraping are some of the ways fraudsters use to supplement the data in hand. They also use this stolen data to understand the data-driven fraud prevention mechanisms that businesses deploy. Since they accurately know the defense parameters and how these mechanisms work, fraudsters can easily devise ways to evade detection and circumvent these fraud prevention solutions.
How Attackers are Defeating Fraud Defenses
Fraudsters not only leverage data but also mobilize all resources—bots, human sweatshops, and a combination—to improvise their attack tactics. They spoof IP and location details, clone device intelligence, and fool behavioral analytics to skip detection.
As a result, attacks are increasingly becoming more complex, and fighting these ever-evolving frauds is becoming a bigger challenge for businesses. Therefore, there is a pressing need for a comprehensive fraud prevention strategy that looks beyond online fraud mitigation and provides holistic protection against myriad forms of fraud.
Businesses today depend on digital identity intelligence to harness insights and distinguish between genuine and fraudulent activity. They use data-driven decision engines that look for clear signals of ‘trust’ or ‘mistrust’. This approach has inherent limitations, as fraudsters have manipulated digital identities at scale. They can impersonate true users and fool businesses to allow them in the ‘trusted’ category. On the other hand, true users can land in the ‘mistrust’ category due to their unpredictable behavior. Should businesses choose to step-up vigilance through out-of-band or multi-factor authentication controls, the user experience gets disrupted. And if they resort to manual reviews, transactions can slow down and lead to loss of revenues. In all of this confusion, businesses block legitimate users, while fraudsters can succeed in their attempts.
Machine learning is a promising technology when it comes to analyzing data for fraud prevention. Unfortunately, that’s true only in theory. Machine learning requires large volumes of data to train the algorithms. It is both tedious and time-consuming to prepare data and subsequently train the machine learning models. In its Cool Vendors in IAM and Fraud report, Gartner observes the significant effort often required to train machine learning models and the need for solutions that decrease the time-to-value.
Need for a Next-generation Fraud Prevention Approach
Businesses today need a robust fraud prevention approach that puts an end to the constant cat and mouse game between them and the fraudsters, for good. They need a long-term approach that disrupts fraud and stops large-scale attacks. This can happen only when the incoming traffic is subjected to secondary screening. This secondary screening augments data-driven insights to determine with absolute confidence whether the traffic is legitimate or originates from automated bots, human sweatshops, or skilled lone fraudsters.
Arkose Labs analyzes incoming traffic and accurately identifies bots and malicious humans from true users using proprietary 3D challenges. This tiered approach does not block any user. Depending on the risk assessment of each user, the platform presents adaptive 3D challenges. By solving these challenges, all users can prove their authenticity. While good users clear them easily, bots fail immediately. Malicious humans and sweatshops must spend more time and invest in resources to clear these challenges at scale. This depletes the economic incentive and makes the attack progressively unattractive, forcing fraudsters to give up and move on.
Gartner Recognizes Arkose Labs for Going Beyond Mitigation-Focused Strategies
Arkose Labs’ zero tolerance to fraud approach provides businesses with a fraud prevention strategy that looks beyond data-driven online fraud mitigation intelligence. It helps businesses detect fraud even when digital identifiers have been compromised en masse. It ensures businesses do not continue to absorb fraud as a cost of doing business.
In fact, Arkose Labs’ next-generation fraud prevention approach increases good user throughput without disrupting the user experience. The insights from user sessions inform the adaptive step-up challenges to continuously evolve according to the changing attack techniques. This enables businesses to ward off evolving threats with confidence.
We believe this is the reason Gartner, in its recent report, has named Arkose Labs as a 2020 Cool Vendor. Read more about what Gartner says about Arkose Labs here.