Retail fraud is growing at an alarming rate. Online transaction fraud is expected “to more than double to $25 billion by 2020,” and 6 out of every 10 retail businesses report “experiencing the same or more fraudulent losses online compared with a year ago.”
As retailers grapple for solutions, the good news is that major components of fraud can be identified and addressed by powerful fraud prevention services.
Retail fraud prevention is focused on four elements
While there are numerous types of fraud and exponentially more factors that can identify it, more effective fraud prevention solutions should quickly and effortless identify red flags in these four key areas:
- Digital Identity centers on device usage. Mobile devices, PCs and tablets carry digital indicators and are normally associated with a primary user of the device. Effective fraud prevention tools will analyze a device’s characteristics alongside known identity elements.
- Personal Identity hinges on verification and authentication. Verification tools can indicate the likelihood a shopper is a genuine person by matching, name, address, social accounts and other pieces of personal information via an extensive database. Authentication helps determine whether the user is who he or she claims to be through single- or multi-factor methods. Online, these can take the form of usernames and passwords, security questions, and/or confirmation communications sent to a verified device. In e-commerce transactions, personal identity combines with digital identity to enable verification.
- Reputation precedes an approaching customer, forming the third pillar of fraud prevention. Establishing it relies on having access to and analyzing historical data on transactions and accounts. Irregular habits or other patterns of erratic behavior may require additional scrutiny.
- Identifying Suspicious Ecommerce Transactions is the final, major component of fraud prevention. The ability to analyze them immediately is where quality verification software comes into play. Suspicious transactions can take numerous forms, from abnormally large purchases or a high number of small transactions in a certain period, to the use of an unusual number of different credit cards by the same person.
Discover how powerful machine learning tools can learn from patterns and behaviors seen on e-commerce sites
Advanced fraud prevention solutions can detect these factors… but it must be quick and seamless
Powerful fraud prevention tools utilize credit profiles, appended data and advanced behavioral analytics to effectively verify customers online or in-store while reducing false positives.
This advantage grows when “new breed” machine learning solutions are used; standard “rules-based” solutions still need a human to change the algorithms, whereas machine-learning platforms can automatically adapt to new activity.
Both the quality of the data and the algorithms that analyze it are constantly evolving to meet emerging threats and include:
- Deeper, broader data that spans both real and world digital identities, transactional behaviors, and elements that assess reputation and fraud risk at the point of origination
- Predictive models and scores that interpret data in search of anomalies and patterns of risk
- A single system for holistic fraud and compliance, increasing user confidence and decreasing costs
- Real-time insights across all channels that don’t slow down transactions or cause unnecessary friction, which enables a better customer experience and fewer false declines.
Accuracy and speed are essential when it comes to effective fraud prevention – comprehensive data and analysis must identify and prevent fraud while also maintaining a good customer experience.
The math is simple: If a fraud prevention solution effectively reduces the 1.58% of the revenue retailers lose to fraud, but loses equivalent or greater revenue by diminishing the customer experience — which results in a higher rate of shopping cart abandonment — it’s not really a solution at all.