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Danger! High-risk TX’s invading.

January 3, 2023
Danger! High-risk TX’s invading.

Identifying High-risk Transactions

When businesses conduct their sales online, they open themselves up to more opportunities and of course, more direct and indirect threats that might bring about damages. Nowadays, more businesses are becoming proactive in preparing themselves to face the difficulties that a new environment brings.

Online selling platforms are often embedded with various payment methods to meet wide customer needs. Not only have these diverse methods allowed merchants to reach a broader customer pool, but also drive them towards operational efficiency backed by new and innovative technologies.

However, coupled with convenience are also risks that might or might not be seen merely on the surface. Ever since the launch of many e-commerce platforms, crimes related to transactions on them have quickly developed over the years, especially payment fraud.

What constitutes a high-risk transaction and customer?

A high-risk transaction is one initiated by a customer that is deemed to be a potential threat to your business. These threats, within the online environment, relate to different types of fraud, cybersecurity issues, and a lot more.

There are several activities that risky customers or users are often caught involved in:
● Taking over someone else’s account/ payment card
● Stealing or use a stolen credit card
● Laundering money
● Signing up for account(s) using counterfeited or stolen IDs
● and more (of course)

Why is it important to identify high-risk customers?

When selling online, it’s extremely common for merchants to come across bad customers who abandon their orders, get involved in payment or chargeback and return frauds, and so on. Dealing with them can take much time, effort, as well as finance, which in turn makes it beneficial to distinguish them from the good ones.

While most merchants know that bad customers exist, they are only recognized after the damage is done. Therefore, learning about your customers and knowing who they are is one thing, the more important thing is to prevent the damages in advance.

This could also be perceived as a strategy to segment customers into different sub-groups of the goods and the bads. Despite having the same rationale for gaining more in-depth information about customers, businesses often take different routes in rolling out their plan on identifying high-risk customers.

Being able to identify these actors help merchants reduce fraud, boost their compliance, maintain high security standards, and support community safety in the long run. To identify risky customers, firms must start with understanding their types, as well as gathering sufficient information data for analysis and patterns seeking.

What are the common types of 'high-risk customers'?

There are several types of high-risk customers working in the e-commerce environment, in which businesses must be more informed about in order to themselves and their customers.

Below are the common types of actors that are considered high-risk to merchants.

Credit card stealers

These are the high-risks who go about stealing or purchasing stolen credit cards to make purchases. If the act stops right at the purchase, the merchant is supposedly fine. However, when the rightful cardholder discovers the unwanted charge and requests for a chargeback.

In that case, the merchant will have to:
● Provide a chargeback for the rightful cardholder
● Lose the item to the fraudster
● Suffer from the risen risk and fraud scores
● Pay the bank for administration fees
● Being reputed for fraud vulnerability

At a glance, when being filed for a chargeback, firms generally feel cheated on, however, when looking more into it, there are a lot more costs to bear under the surface.

These cases, however, can be spotted using data retrieval and analysis. This is because all online transactions are somehow linked to the provision of personal information, including payment card details. Other than that, device and browser information such as account location, IP address, abnormal transaction patterns can help reveal a lot of information about the card and the person using it.

Based on those retrievable data points, card stealers can be spotted if the IP address and account location appear to be different from past behaviors.

Money laundering customers

This is recognized as a serious crime in the financial world, money laundering not only brings harsh punishment for the criminals, but also the institution where the crime was committed.

When adopting specialized softwares or tools to monitor transactions, many abnormalities can be preset for flagging. Transactions happening in sensitive or sanctioned countries can be flagged for rejection or further manual review. When a payer’s IP address points to a location that is deemed easily prone to money laundering activities, the system can easily flag the suspicious transaction.

Multi-account customers

Some customers take great advantage of account signups to register for many accounts under different names, which can also derive from fake or made-up personal information. This is usually aimed at abusing referral programs to gain from discounts or special offers. Many of these customers are participants of sophisticated fraud rings that cause huge damages to attacked businesses.

Multi-account customers are often spotted during the registration phase, using the retrieved data from both the customer and the device, past behaviors. With that said, sufficient retrieval and collection of data points would definitely help unveil risky behaviors or accounts.

To wrap up

Owing to the growing complexity of online environments, customer, or card holder identification becomes more urgent and should constitute a fundamental part of business strategy.

To meet the needs and concerns of e-commerce participants, many fraud prevention programs are now available at great rates for adoption. Solution providers also offer a wide variety and combination of functions and features to support the work of unveiling fraudulent attempts in transactions.