What is meant by online fraud?
Online fraud – also called e-commerce fraud – occurs when someone uses stolen or fake information to make an online purchase.
Understand the different types of online fraud
There are many types of e-commerce fraud, but here are four of the most common:
Account Takeover Fraud (ATO):
ATO attacks occur when phishers use stolen identities, bot attacks, phishing, malware, and other tools to gain user credentials and control an e-commerce account.
After the account is hacked, the criminal can transfer funds, make account purchases, modify the account, or even target other accounts of the victim. A sudden increase in logins, closures, and changes to account profiles can indicate potential ATO attacks.
Misuse of the first party
Often referred to as “friendly fraud”, this type of fraud has a financial impact on merchants although it is often not harmful. This occurs when a purchase is made online by the cardholder or a family member, such as a child.
Then, the cardholder forgets that they have made the purchase, or is unaware of the purchase made by a family member, and informs their bank that it is a chargeback fraud.
Card test scam
In this common type of credit card fraud. When malicious people get access to stolen credit card account numbers, they often use scripts or bots to quickly make multiple online purchases to verify that accounts are still valid and confirm associated credit limits. Before small trial purchases are revealed, criminals make several large purchases, usually up to the available balance in the accounts.
Third party scam
Also referred to as third party abuse, it is one of the most common types of e-commerce fraud. It happens when a bad actor gains access to stolen payment information, such as a credit card number, and uses it to make an online purchase.
When the actual cardholder learns of the unauthorized purchase, he reports it to the bank which results in a chargeback to the merchant.
Fraudulent activities like this can be greatly reduced by using a proper fraud prevention solution. For example, those who use advanced artificial intelligence techniques and learn from a vast network of data are able to review online purchases and discover patterns that indicate whether the activity is real or fraudulent.
These solutions work as follows: when you initiate an online purchase, they analyze many aspects of the transaction such as who initiated the purchase, the device being used, the product being purchased, and the card being used.
Then, when the system detects suspicious patterns, it alerts you that your purchase has been flagged as potential credit card fraud so you can prevent the transaction from proceeding.
Signs that your business is at increased risk of online fraud
Neglecting certain practices can put your business at greater risk of e-commerce fraud.
For example, companies must monitor the source of traffic on their websites, track sales and chargebacks, monitor fraud complaints from customers, and look for changes in the buying patterns of existing customers. Without this necessary oversight, businesses and their clients are more likely to be targeted by criminals.
Additionally, organizations must remain aware of current fraud trends and must speak with their partners and service providers to develop strategies that specifically address threats as they evolve.
How to evaluate technical solutions to fraud
You know that fraud prevention is important to your e-commerce business, but where do you start?
Start with an inventory of what you need: What threats does your business face? What tools do you currently use to limit fraudulent activities? Are these tools able to protect your business from current threats and new activities?
Next, look for a comprehensive fraud protection solution that uses machine learning to detect new threats as they arise.
Additionally, look for these capabilities when evaluating a Fraud Protection solution:
Helps protect your revenue by increasing bank acceptance rates and reducing payment friction that can result in abandoned carts.
Prevents fraudulent account access, fake account creation, and account takeovers.
Prevents loss by quickly identifying potential revenue and discount fraud for purchases across multiple channels.
Five helpful questions to ask as you evaluate a potential fraud protection solution:
- Are you using machine learning to detect suspicious activity?
- Is he able to develop knowledge about the unique behavior patterns of customers?
- Do you use machine learning algorithms to report suspicious transactions?
- Can she self-educate and increase her own knowledge as clients’ activities change?
- Are you using machine learning algorithms, rather than a rule-based approach, to evaluate transactions in real time?