FaaS demonstrates how fraud is evolving and how the technology that makes lives easier for customers simultaneously makes fraud easier for criminals. Businesses can help protect their consumers, brand reputation, and revenue by knowing how FaaS works and implementing best practices to prevent it.
The widespread shift to online shopping, digital banking, and other services was beneficial to consumers, but it also provided opportunities for organized fraudsters to grow and expand their services to include Fraud-as-a-Service (FaaS). FaaS comes in various forms, all to make fraud easier for both seasoned and rookie criminals.
FaaS organizations offer customer service and support, and they also offer money-back guarantees and free trials along with research, development, and training in effective types of fraud. FaaS providers are usually found on the dark web, operating from regions and territories where they are less likely to be shut down or prosecuted.
Detection and Prevention of Fraud-as-a-Service Attacks
Best practices for preventing fraud in businesses are more crucial than ever. This is an excellent opportunity to double-check that the company’s anti-fraud program covers the following factors:
Data Entry Attempts and Velocity Should Be Limited
The speed with which a bot attack moves is one of the obvious signals. Bots are significantly faster than people at loading carts, checking out, and placing orders. They also repeatedly try alternative passwords and one-time codes until they find one that works.
Setting a limit on the number of attempts before consumers are locked out helps safeguard the company from bots if the website allows users to make limitless attempts to submit their data accurately. Similarly, separating busy customers who are reordering familiar things from botnets built to sweep up as many items as they can, as quickly as possible, can be done by flagging orders for velocity.
Every Order Should Be Screened
Businesses can no longer assume that returning customers are who they appear to be since billions of compromised credentials are available to cybercriminals, FaaS scams are gathering more credentials, and FaaS bots can exploit those credentials to crack accounts at scale.
That means it’s no longer safe to authorize orders from known customers automatically. To flag the order as suspected account takeover fraud or help validate the customer, every order must be scanned for payment data and geolocation, device, and behavioral biometrics.
Conduct Large-Scale Batch Analysis to Detect Fraud
Thanks to bot rental and hacked credentials, fraudsters can get creative with their attacks on enterprises. A gang, for example, could hit an online store with a flurry of orders that appear to come from several consumers using various payment methods. Each of these orders may pass the requirements of the fraud screening solution and be accepted.
The fraud solution may identify patterns that indicate criminal behavior if the merchant also picks random orders for assessment as a group. Perhaps that flurry of orders from various consumers was delivered to the same address. Maybe all of the cards they used to pay had the same bank identification number, implying that the customers were fake. These flaws can be discovered using batch analysis, allowing fraudulent orders to be canceled before shipping.
Continuously Train the Machine Learning Algorithm
The outcomes of manual reviews can and should be fed into the Machine Learning algorithms of automated fraud solutions. This aids the AI’s ability to recognize sophisticated fraud and customer behavior that isn’t entirely typical but isn’t fraudulent. Over time, this may result in fewer flagged orders and a reduction in the requirement for manual review.
Track Brand Mentions
Consumers are often duped into revealing their credentials and even authentication codes by FaaS methods that spoof brands. Every company should look for impersonated social media profiles, email campaigns, websites, and even SMS marketing. Companies can report imposters and warn their customers that a scam is running under their brand.
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