Using Behavioral Analytics to Detect Ecommerce Fraud
Explore how security teams extract meaningful insights from behavioral analytics data and prevent ecommerce fraud.
Ecommerce fraud is a significant issue that both large and small companies often suffer. In 2016, fraud increased to 33% compared to 2015 according to Experian data.
As online sales continue to rise, Card Not Present (CNP) fraud is set to not only keep the pace, but grow to an even bigger problem for ecommerce merchants. CNP fraud accounts for 60% to 70% of all card fraud, according to a September 2017 Juniper Research report.
As businesses race to capture more online sales, fraud prevention teams and companies are turning to web analytics data, such as behavioral data, but these same fraud prevention teams and companies are struggling with how to abstract meaningful insights that can be leveraged in their current fraud prevention platforms.
How behavioral analytics detects fraud:
The analytics system analyzes the repository of data which are used to detect patterns, anomalous behavior, and other intent signals that can provide actionable insights into both legitimate and fraudulent transactions.
Have you optimized your use of behavioral analytics for real-time fraud prevention?
Although Pageview analytics continue to be a valuable tool for ecommerce marketing departments, fraud prevention teams and companies have been limited in their ability to provide insight and actionable data. Moreover, they are not technically positioned to create rich features which can be leveraged in machine learning and statistical models.
Fraudsters can and will exploit weaknesses which is why most companies turn to fraud prevention providers, having the expectation that their technology is outpacing that of the criminals perpetrating the fraud. It is important for businesses to partner with a provider that has the ability to identify and react to untapped data sources such as behavioral analytics.
Detecting unique behaviors using a flexible fraud detection software is the key to ensuring the data is actionable.
Behavioral analytics solutions are designed to understand the normal behavior of each individual account holder, calculate the risk of each new activity and then choose intervention methods commensurate with the risk. The key characteristics that make behavioral analytics effective are automatically monitoring all activity for every account holder in real-time, not just customers that convert.
It is crucial to keep a close eye on the behavioral pattern of a normal customer versus a fraudster. Precognitive’s behavioral analytics engine, Precog-BA, focuses on the user activity occurring on your digital properties to build a consumer journey that can be leveraged in the risk decision.
Business leaders and marketers have traditionally utilized behavioral data to optimize the user experience with the end goal of converting the user to a sale, failing to realize how the same data can also be used to identify fraudulent behavior on the website. By incorporating device intelligence and behavioral analytics, our software delivers accurate and reliable decisions letting you know whether to allow, review, or reject at the time of a transaction.
The earlier fraudulent activity is detected, the easier and less costly it is to prevent. Behavioral analytics will detect the early stages of a fraud attack, before a transaction is initiated. For example, it will detect anomalous behavior such as changing contact information prior to initiating a CNP transaction. Because it is based on behavior, it will detect abnormal activity regardless of the type of attack, even newly emerging schemes.
Ecommerce fraud is becoming more invasive each year. It’s important to choose a fraud prevention software that identifies fraud well before the fraudster attempts to complete a CNP transaction. At Precognitive, our flexible design allows you to utilize our technology to ensure detection of emerging fraud while keeping your real customers happy.
To learn more about Precognitive’s multidimensional fraud software, click here.