As featured in Forbes, Nov. 7, 2019

 

  • There has been a 680% increase in global fraud transactions from mobile apps from October 2015 to December 2018, according to RSA.
  •  70% of fraudulent transactions originated in the mobile channel in 2018.
  • RSA’s Anti-Fraud Command Center saw phishing attacks increase 178% after leading banks in Spain launched instant transfer services.
  • Rogue mobile apps are proliferating with, 20% of all reported cyberattacks originating from mobile apps in 2018 alone.

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Exploring The 7 Ways AI Is Reducing Mobile Fraud

Where rules engines are best suited for spotting existing trends in fraud activity, machine learning excels at classifying observations (called supervised machine learning) and finding anomalies in data by finding entirely new patterns and associations (called unsupervised machine learning). Combining supervised and unsupervised machine learning algorithms are proving to be very effective at reducing mobile fraud. The following are the seven ways AI and machine learning are reducing mobile fraud today:

  1. AI and machine learning reduce false positives by interpreting the nuances of specific behaviors and accurately predicting if a transaction is fraudulent or not. Merchants are relying on AI and machine learning to reduce false positives, saving their customers from having to re-authenticate who they are and their payment method. A false positive at that first interaction with a customer is going to reduce the amount of money that they spend with a merchant, so it’s very important to interpret each transaction accurately.
  2. Identifying and thwarting merchant fraud based on anomalous activity from a compromised mobile device. Cybercriminals are relying on SIM swapping to gain control of mobile devices and commit fraud, as the recent hack of Twitter’s founder Jack Dorsey illustrates. Hackers were able to transfer his telephone number using SIM swapping and by talking Dorsey’s mobile service provider to bypass the account passcode. Fortunately, only his Twitter account was hacked. Any app or account accessible on his phone could have been breached, leading to fraudulent bank transfers or purchases. The attack could have been thwarted if Jack Dorsey’s mobile service provider was using AI-based risk scoring to detect and act on anomalous activity.
  3. AI and machine learning-based techniques scale across a wider breadth of merchants than any rules-based approach to mobile fraud prevention can. Machine learning-based models scale and learn across different industries in real-time, accumulating valuable data that improves payment fraud prediction accuracy. Kount’s Universal Data Network is noteworthy, as it includes billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and multiple payment networks. That rich data feeds Kount’s machine learning models to detect anomalies more accurately and reduce false positives and chargebacks.
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