For years, fraudsters hunted for numbers from credit cards and printed them on blank ones to use at brick-and-mortar stores. According to researchers, it takes a long time, sometimes more than a month, to detect fraud that occurs at brick-and-mortar establishments.
Literally everyone has heard about (or experienced) money being stolen from credit cards by unauthorized scammers.
Thus, as we attempt to navigate a maze of modern technologies, it's getting harder to effectively react to changes and secure the security of one of our most important resources – money.
For years, fraudsters hunted for numbers from credit cards and printed them on blank ones to use at brick-and-mortar stores. According to researchers, it takes a long time, sometimes more than a month, to detect fraud that occurs at brick-and-mortar establishments.
However, fraud also threatens companies that provide online services, including those in the financial sector. For example, 1 in 5 people changed their banks after experiencing scams.
The challenge for industry players is to highlight and reinforce fraud detection.
Machine learning is the technology of our time when it comes to shielding us from fraudulent activity.
Machine learning algorithms can be your sustainable partner in uncovering correlations between customer behavior and fraudulent actions. Other machine learning benefits are faster data management and fewer manual actions.
In order to reduce vulnerability in the financial services industry, fraud prevention must be a strategic target. Researchers discovered that a solution enriched by machine learning can detect nearly 95% of all fraudulent activity.
These facts prove the benefits of using machine learning in anti-fraud systems.
As a unique anti-fraud solution, Evinent Analytics is able to detect suspicious activity for any company within the financial sphere, including banking, insurance, and other fintech area operations. All you need to do is provide a sufficient data set to satisfy program properties.
