Modern Fraudsters are Fast – Can Your System Keep Up?

Any organization conducting business online is aware of the risk of fraud and the potential penalties for having too many chargebacks. But not all businesses are aware of just how fast and sophisticated fraudsters have become at committing online fraud. And there are many different types of online fraud- credit card fraud, e-gift card fraud, promo abuse, new account fraud, account takeover (ATO) fraud, to name a few.
Bot Attacks are on the Rise
For many Fraudsters, bots are the go-to method for committing fraud. For example, e-gift card fraud is one of the fastest growing forms of online fraud. Last year, Distil Networks released a report about GiftGhostBot, a bot that targets e-gift card balances and processes. GiftGhostBot can test up to 1.7 million gift card account numbers per hour.
Botnet attacks are not only occurring more often but also growing in scope and size. According to an IBM Security Intelligence blog post, there were 210 million attempted fraud attacks in the first quarter of 2018 alone, and the attacks involved 1 billion bots. The Q1 figure represents a 62% increase in bot-involved fraud attacks from 2017. The blog post also reports that 100 million of these bot attacks originated from mobile devices.
Rules-Based Systems Are No Match for Fraudsters Armed with Bots
Fraudsters are using technologies like bots that allow them to commit many types of online fraud in a matter of milliseconds. Humans can’t keep up with the size, speed, and scope of botnet attacks, and rules-based systems rely heavily on manual input from humans. Any fraud prevention system that relies on humans creating and maintaining the rules of that system will be no match for fraudsters who are armed with bots.
All is not lost if you have a rules-based system for detecting and preventing fraud. Machine learning (ML) models can be trained to detect fraudulent transactions at a pace that can keep up with fraudsters ever-changing tactics and technologies. And rules-based systems can be enhanced with ML models. With machine learning and the right data, your system can provide an effective counteroffensive against fraudsters and vast networks of bots.
You Need Data Designed for Risk
Adding machine learning models to rules-based fraud detection systems is not enough. You also need to feed the models the right data, and Ekata data is designed specifically for risk. We provide a suite of identity data APIs that are designed to improve the performance of fraud detection models. And our data is comprehensive and reliable- features are never changed in a way that would break your ML models.
Machine learning is how a fraud detection system keeps up with fraudsters, but the right data is how a system effectively catches real fraud.
Can Your System Keep Up?
If your company is conducting business online, then you likely have a fraud prevention system in place- but what kind of system? Is it a rules-based system, a machine learning-driven system, or perhaps a bit of both? Are you feeding your system the right data?
The answer to these questions is crucial as to whether your fraud prevention system can not only keep up with the fast and sophisticated tactics of modern fraudsters but also effectively detect and prevent real fraud.
Need help adding machine learning and the right data to your rules-based fraud detection system? — Our team of machine learning solutions architects is happy to help–you can contact them here.

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