E-commerce fraud in the digital age has become increasingly sophisticated, demanding businesses to adopt advanced preventive measures and strategies to ensure the security of their online transactions.
In the digital world, fraud may occur from different directions. To prevent themselves from becoming victims, institutions, merchants, and vendors need to embrace multi-layered fraud prevention strategies using behavioral analysis and fraud analytics.
This approach deals with both the usual fraud as well as the more sophisticated fraudulent attacks.
Online fraud continues to be a costly problem for merchants and it is getting worse. Internationally, the loss of revenue due to fraud instances has doubled in recent years, and this increase in revenue loss due to fraud is mainly because of an increase in electronic commerce. People buying and selling or trading online using their own or different market platforms are exposed to digital fraud. Fraudsters keep on innovating and refining their fraud techniques.
Behavioral analytics, especially behavioral fraud analytics, has long been an effective way to catch out fraudsters, but recent advancements in technology and tools have made this approach even more accurate and scalable.
Merchants and vendors need a comprehensive anti-fraud strategy, and tools that employ this method of detection are fast becoming a necessity.
Now the question is how behavior and fraud analytics are being used to prevent electronic commerce or e-commerce fraud.
E-Commerce Fraud in the Digital Age
By utilizing behavioral and fraud analytics techniques the institutions may better track and analyze different transactions and related datasets to establish predictable patterns of user behavior over a specified period.
When data that does not fit the fraud pattern is detected, it indicates anomalous behavior that may be an indicator of fraudulent activity. Institutions, merchants, and vendors should be able to expect somewhat consistent, predictable user behavior from the average customer using behavioral and fraud analytics.
Customers who do not behave in the desired way are not like regular customers or shoppers, which means they might be fraudsters or at least their transactions need to be scrutinized in more detail such as detailed analysis of customers’ transactions and buying/selling behavior, complaints or claims lodged by the customer over a while, income spent on shopping over a specified time, specific products purchased by the customer on a repeated basis, etc.
A fraudster might immediately load up their cart with large quantities of an expensive product with a high resale value, whereas a normal customer usually clicks on similar products with lower prices before making a final decision. These signs may be obvious enough if you’re looking closely at a single transaction, but automated tools make it possible to spot these red flags even as hundreds or even thousands of transactions are being processed daily.
Similarly, institutions may use employees’ behavior data including the employees who committed or attempted fraud in the institution. The unsuccessful fraud attempts made by employees need to be analyzed in detail and compared with the behavior or conduct data of such employees. The purpose is to identify data trends to uncover hidden frauds or unidentified fraud attempts.
Using behavioral and fraud analytics, some fraud indicators are easy to detect and scrutinized. For example, it is not uncommon to see fraudsters change shipping address information right before a transaction, or even after the transaction has already been processed. However, rules-based tools may still miss the subtler signs of fraud, which is why machine learning and AI features have become strong selling points for fraud prevention solutions.
Final Thoughts
In our ever-evolving digital realm, fraud has proliferated, compelling institutions, merchants, and vendors to bolster their defenses with multi-layered strategies. Given the escalating threats, it is now imperative to harness the advancements in behavioral and fraud analytics, tools that identify and decode patterns, spotlighting anomalies indicative of potential fraud. E-commerce’s booming trajectory has inadvertently augmented its susceptibility to fraud, thus making the role of analytics even more paramount.
This meticulous analysis of user behaviors, be it customers or employees, can unearth deviations, such as unusual purchasing patterns or transactional alterations, which might be cloaked attempts at deception. As the sheer volume of transactions burgeons, automated tools, fortified by machine learning and AI, are not just beneficial but essential, ensuring subtle signs of deceit aren’t overlooked in the vast digital sea.
