When e-commerce merchants are looking to select a fraud protection solution, they may be wondering if they should be looking for one that analyzes all e-commerce transactions — or only those that look like fraud?
The first approach certainly takes more time and money, but it's the most efficient way to ensure fraudulent transactions aren't approved. The second option requires less labor and can be managed by regular fraud filters, but it leaves merchants open to orders that appear legitimate but are actually fraudulent.
In today's digital market, most transactions are legitimate. However, it's the ones that aren't that merchants must protect themselves against. Because it's important to strike the right balance between speed and efficiency to protect profits and brand reputation, let's evaluate both approaches.
The Risks of Selective Transaction Analysis
When companies use artificial intelligence to analyze only those transactions that appear fraudulent, they're taking a big risk by:
Being Unable to Differentiate Between Fraudulent and Legitimate Orders
Inexperienced merchants may mistakenly approve fraudulent transactions that seem legitimate.
Rejecting Too Many Transactions
A low fraud rate could merely mean a high number of rejections, which could have a negative impact on your reputation.
Ignoring the Changing Face of Fraud
Traditional strategies based on artificial intelligence may fail to detect new fraud trends. When fraudsters evolve their tactics, the rules of artificial intelligence don't always keep up, rendering fraud control solutions inefficient.
Not Understanding How Fraudsters Play the System
Fraudsters are very good at slipping through fraud filters unnoticed. If they find out that a merchant analyzes only transactions that are more than $1,000, they'll keep their orders to less than $999 so they're automatically approved.
Implementing Too Many Fraud Filters
Merchants typically have numerous rules in their fraud filters to flag as many fraudulent transactions as possible. However, this can also increase risk exposure. If merchants aren't careful about the order in which the filters are used (first, second, etc.), some of these rules may cancel each other out, reducing the amount of protection.
Failing to See the Big Picture
Focusing on individual transactions, rather than the whole, may make preventing large attacks even harder. For example, a customer buying a laptop for delivery on a specific street may appear legitimate. However, if the merchant isn't looking at their transactions as a whole, it may not realize that 10 laptops were delivered to different addresses on this same street, all within a short period. This may be an indication of a fraud attack.
The Advantages of Analyzing all Transactions
It's clear that manually flagging transactions for analysis might not be the right answer. Instead, it may be better to analyze every transaction, using additional, complementary tools. How can this approach benefit merchants?
Understanding That Low Cost May Still Indicate Danger
Fraudsters tend to place low-cost order to make sure a stolen credit card really works. If it does, this opens the door to larger fraudulent orders. Analyzing all transactions lets e-commerce retailers detect small purchases designed to test the fraud prevention system -- transactions that might go unnoticed otherwise.
Facilitating an Overall View
The more transactions merchants examine, the greater the amount of transaction data that can be used in the future to make more informed solutions. Consider the previous example of laptops. Each individual sale may appear innocent, but the pattern becomes visible when all transactions are viewed together.
Taking a Closer Look at Doubtful Transactions
Some transactions may fall into a grey area: not obviously legitimate or fraudulent. Fraud control systems based solely on artificial intelligence will automatically reject all these orders. But a system that investigates and analyzes these transactions will alert retailers about which orders are good and should be approved.
Avoiding Large-Scale Fraud Attacks
Professional fraudsters work in concert, orchestrating coordinated attacks that can happen over a short period of time. If a single company analyzes the order, it's like throwing cold water on a fire: attacks can be identified, interrupted and avoided far more quickly.
Artificial intelligence brought major progress to fraud control; however, companies cannot rely solely on this technology to differentiate between which orders are legitimate and which are fraud. Instead, companies that combine artificial intelligence with a team of trained analysts find that they become smarter and more efficient, approving the right transactions, increasing customer satisfaction and capturing customer loyalty.
Implementing a Comprehensive Fraud Prevention Solution
Because fraudsters are continuously improving their tactics, it's important for online merchants to develop a comprehensive fraud prevention approach that includes:
- Advanced technology to quickly accumulate data
- Statistical intelligence to determine which data patterns are suspect and require detailed analysis
- Sophisticated human analysis to help companies develop a broader view to increase order approval
This approach is ClearSale's distinction.
Technology, while impressive, is not enough to fight fraud. When merchants have a more inclusive view of their sales, they can increase the accuracy of their fraud detection and be up-to-date in a constantly changing market.
ClearSale combines the analysis of large volumes of data, statistical intelligence and human brainpower to offer the optimal balance between fraud protection and greater sales. Contact our fraud protection analysts today to learn why our unique combination of trained analysts and state-of-the-art machine learning can help your e-commerce business stay one step ahead of fraudsters.