International Revenue Share Fraud

Early Detection to Prevent Costly IRSF Losses

The problem with IRSF is that oftentimes it is not detected until it is too late and fraudsters have already done their damage. It’s challenging for businesses to get visibility into these attacks early enough to prevent them, as fraudsters have access to a range of tools that can help them bypass identification systems.

However, businesses can thwart these attacks by utilizing robust anti-automation technology. Fraudsters rely heavily on automated scripts to carry out these attacks at scale. Businesses should have defenses in place that can detect even human-like bots, and be able to stop them. Since fraudsters often evolve their attacks to overcome known defenses, businesses should have in place a security platform that can learn and change to adapt to evolving attacks.

Bots keep getting smarter and more difficult to stop, so businesses should have fraud defenses in place that are resilient to even the latest innovations in offensive machine learning. By accurately detecting advanced bots, and having the means to stop them, businesses can ensure that IRSF attacks are stopped.

Beyond that, knowing unused PRN ranges to predict the possibility of an IRSF attack, along with access to phone intelligence data is useful to see if the same numbers were used elsewhere to commit IRSF, are helpful tools in fighting these attacks.

The Arkose Labs Approach

Businesses can insert Arkose Labs at any touchpoint which is protected by one-time passwords. such as the login flow or new account registration, in order to detect fraudulent traffic. Furthermore, due to Arkose Labs’ unique in-session authentication that combined real time risk classification with interactive challenges, businesses will be able to rely less on multi-factor authentication methods.

Accurate Classification:

Arkose Labs analyzes hundreds of data points around device, network, behavior and location to create “telltales” to determine whether traffic is suspicious or not. This is partially informed by data from our global network, where we create telltales of known previous attacks. Arkose Labs also can implement industry-specific telltales customized for individual merchants based upon their business model and industry Traffic is then segmented based on whether it is likely to be legitimate, a bot or a malicious human, which informs the platform of any required secondary screening and the type of enforcement challenge shown.

Challenge and interact:

To understand the intent of traffic in a deterministic way, secondary screening must be paired with the risk assessments stage. The platform tests and challenges high-risk traffic using interactive technology designed to foil even the most advanced automation. Meanwhile, increasingly complex challenges are served to human attackers, which increases the amount of time it takes them to complete a task, thus reducing their ROI. This leads them to abandon their attacks. The platform tailors the type of challenge based on the nature of the attack.