The COVID19 pandemic has created an environment where online fraud is thriving. With retailers scrambling to handle unbelievably high volumes of online transactions and fulfill an ever-growing number of online orders, avid fraudsters who have experienced the crippling effects of the shrunken global economy are looking for soft targets.
Companies that are detecting fraud manually or at least trying to detect fraud under these circumstances manually are losing money on both ends. Not only are their expensive manual efforts useless against determined and technologically advanced fraudsters, but their bottom lines are also suffering directly because of these unsuccessful fraud mitigation strategies.
The Shocking Costs of eCommerce Fraud
Between 2020-2021, eCommerce fraud reportedly increased by 18%. Cybercriminals exploited the COVID19-fueled increase in online shopping to steal over $20 billion from unprepared merchants. However, the ‘true cost’ of eCommerce fraud is actually much higher. The true cost is much higher when we consider factors like –
- Stolen goods
- Failed chargeback attempts
- Operational costs of tackling eCommerce fraud
- Vendor costs (fines, lower transaction authorization rates, etc.)
- The negative impact of false positives (when eCommerce vendors unintentionally block normal online shoppers fearing they are fraudsters)
The true cost of eCommerce fraud is reportedly equivalent to 10-15% of the value of all eCommerce transactions. In 2021, the global eCommerce market was forecasted to reach a valuation of $5 trillion.
If you do the math, eCommerce fraud (and all the associated factors) can potentially cost the industry $500-700 billion over the next few years! Those figures are bigger than the GDPs of countries like Austria, Norway, and Switzerland!
No industry is suffering from digital fraud as much as the online retail industry. What online sellers need to realize is that there’s no one-size-fits-all approach to securing eCommerce platforms. Successful fraud prevention strategies need to be customized.
eCommerce vendors also have to maintain the sensitive balance between low approval rates and chargeback fraud attempts as they secure their online platforms. Thankfully, there are technologies that can be customized specifically to address very specific risks. These solutions are powered by Machine Learning algorithms.
The Need for Advanced Online Fraud Prevention Tools
Many businesses are viewing this shocking rise in eCommerce fraud as a chance to reprioritize their digital transformation programs. These businesses need an eCommerce fraud prevention company with tools that use Machine Learning algorithms.
Why? Well, firstly, conventional eCommerce fraud prevention tools that only function along the lines of the specific instructions written by programmers have proven to be ineffective against new fraud patterns. Secondly, eCommerce fraud prevention tools built with Machine Learning algorithms are flexible and smart.
These tools improve themselves whenever there are inputs of new information. As eCommerce fraud strategies evolve and become more complicated, these tools “learn” and become stronger. Both supervised and unsupervised Machine Learning (ML) algorithms are used in these eCommerce fraud detection and prevention tools.
Using these algorithms, the programmers create extremely strong thresholds on their clients’ eCommerce platforms. It’s nearly impossible for potential scammers to break through these pre-established thresholds that determine whether transactions are fraudulent or not.
Here are two critical reasons why Machine Learning algorithms work so well on eCommerce fraud detection and prevention tools –
Real-Time Data Assessment
Unlike traditional fraud detection tools that can only deal with scenarios that’ve happened in the past, ML-powered eCommerce fraud prevention tools conduct real-time risk assessments for all transactions, whether or not they’re ‘suspicious.’ These tools track each transaction within seconds, focusing on data points like –
- The purchase history of the user requesting the transaction
- Related transactions of the user
- The device being used by the shopper
- The behavioral patterns of the user (which links are the user clicking, what webpages the user has visited, etc.)
- VPN detection
- GPS location data
- Payment information
State-of-the-art ML-powered algorithms use all of these determinants to deem each shopper as ‘good’ or ‘risky.’ These algorithms consider many more factors than traditional fraud detection tools to pass extremely accurate judgments.
In some cases, ML-powered eCommerce fraud prevention tools have been able to block spammers and hackers even before they launched attacks on the eCommerce platform.
Finding New Fraud Patterns
ML-powered eCommerce fraud prevention tools are constantly learning and figuring out obscure correlations between data points. With each new fraud threat, these tools become stronger. These tools easily pick up on deviations in user behavior.
On the other side, they automatically verify ‘good’ shoppers who’ve frequented the eCommerce platform many times in the past. So, despite the eCommerce platform getting increasingly rigid against fraudulent transaction attempts, the platform still remains user-friendly.
As hackers become more inventive in the COVID19 era, the need to reject dated software solutions becomes extremely urgent. eCommerce vendors should use the latest protection tools that are ML-powered and ISO:27001 compliant to leverage big data and consistently add new layers of security to their growing eCommerce platforms.
