7 Methods to Reduce Mobile Fraud Before the Holidays

Image by Gerd Altmann from Pixabay

There were 680% more global fraud transactions from mobile apps in December 2018 than in October 2015 according to RSA. 

  •  70% of fraudulent transactions have their source in the mobile channel in 2018.
  • RSA’s Anti-Fraud Command Center saw phishing assaults increase 178% after leading banks in Spain started instant transfer options.
  • Rogue mobile apps are proliferating with, 20% of all reported cyberattacks are from mobile applications just in 2018.

There are about 82 new rogue apps submitted in a day to any given AppExchange or application platform. Mobile and digital commerce are cybercriminals’ favorite methods to attack because they are succeeding with a lot of strategies for defrauding people as well as businesses.

Phishing, malware, smishing, and SMS texts are more effective than emails to gain access to victims’ account credentials, credit card numbers and other personal information.

How you can use AI and Machine Learning for Mobile Fraud Prevention.

Mobile fraud needs to stop with state-of-the-art approaches by using AI and machine learning’s innate power.

The methods which were used so far to thwarting digital fraud rely on rules engines that thrive on detecting and taking action based on known patterns and are often hard-coded into a merchant’s system. Fraud analyst teams further upgrade rules engines to reflect the unique requirements of the merchants’ selling strategies across each channel. Fine-tuning rules engines make them efficacious at finding and taking action on well-known threat patterns. But the problem for every merchant relying on a fraud rules engine is that they often don’t catch the newest patterns used by cybercriminals. Where rules-based methods of digital fraud are not effective anymore, AI and machine learning can be.

7 Ways AI Is Reducing Mobile Fraud

  1. AI and machine learning reduce false positives by interpreting the nuances of certain behaviors and clearly predicting if a transaction is fraudulent. Dealers are counting on AI and machine learning to lower false positives, helping their customers so they don’t have to re-authenticate who they are and their payment method.
  2. Recognizing and thwarting dealer fraud based on anomalous activity from a compromised mobile device. Cybercriminals are using SIM swapping to have control of mobile devices for illegal purposes.
  3. AI and machine learning-based technology are used on a wider breadth of traders than any rules-based method to mobile fraud prevention can. Machine learning-based models are used for different industries in real-time, accumulating important data that improves payment fraud prediction. Kount’s Universal Data Network is notable because it includes billions of transactions over 12 years, 6,500 customers, 180+ countries and territories, and a lot of payment networks. That abundant data gives Kount’s machine learning models the capacity to find anomalies better and reduce false positives and chargebacks.
  4. Using both supervised and unsupervised machine learning algorithms has, as a result of a formidable speed advantage, with fraudulent transactions identified in about 250 milliseconds. Merchants’ digital business models’ scale and speed are increasing, and with the holidays coming soon, there’s a bigger probability that they will set mobile commerce sales records. The dealers who will have the most sales are trying to see how security and customer experience can complement each other. The ability to approve or reject a transaction in a second or less is the base of excellent customer experience.
  5. Knowing when to use two-factor authentication via SMS or Voice PIN to find false negatives or when is not necessary to do so, protecting customer relationships in the process. The capacity to spot anomalies quickly means that customers are not forced to re-authenticate themselves and when transactions are actually fraudulent, losses have been prevented in a second or even less. 
  6. Giving a real-time transaction risk score that combines the capacities of supervised and unsupervised machine learning into one fraud prevention payment score. Dealers need a real-time transaction risk score for all the channels they use to sell and machine learnings have the ability to scale across all channels and provide a transaction risk score in milliseconds
  7. Combining details from supervised and unsupervised machine learning with contextual intelligence of transactions helps fraud analysts to do more investigations and fewer transaction reviews. Merchants are saying AI and machine learning-based methods help with approving and rejecting more orders automatically so they can focus on those gray area orders, helping fraud analysts to do more strategic, rewarding work.