Reducing eCommerce Fraud with Better Analytics

This is the continuation of the article, "Five Tips on How Analytics and Data Can Be Useful for e-Commerce Owners."

Predictive analytics and big data are two ways eCommerce sellers can identify fraud, which is becoming a pressing issue. According to the Global Fraud Report, almost three-quarters of online retailers (72 percent) agree that eCommerce fraud is a growing concern; moreover, 63 percent of them "have experienced the same or more fraud losses in the past 12 months."

Before the arrival of data analytics tools, eCommerce sellers utilize a sample of customer data for fraud analysis. This means spending a lot of time and money to investigate the entire sample because the analysis would have to be manual. Now that big data analytics systems are available, retailers can analyze all data for fraud much quicker.
One way to battle eCommerce fraud with data analytics is to use predictive analytics. Here are the steps involved in this process:

1. Prepare the database of online orders from your store. This means defining a timeframe for orders
2. Define the types of transactions to include in the database. Since eCommerce fraud happens only with credit cards, exclude orders where creating a chargeback is impossible
3. Identify the patterns of fraudulent orders to differentiate between the good and the bad transactions
4. Model the data to teach the algorithm to define suspicious orders based on the patterns. This is where data intelligence professionals and/or tools perform approaches like deep learning algorithms
5. Implement the model
6. Add new fraud patterns to the algorithm.

As a result, it would be possible to reduce the amount of fraudulent orders by rejecting them and prohibiting fraudsters from making purchases.

Before the arrival of data analytics tools, eCommerce sellers utilize a sample of customer data for fraud analysis. This means spending a lot of time and money to investigate the entire sample because the analysis would have to be manual. Now that big data analytics systems are available, retailers can analyze all data for fraud much quicker.

One way to battle eCommerce fraud with data analytics is to use predictive analytics. Here are the steps involved in this process:

1. Prepare the database of online orders from your store. This means defining a timeframe for orders
2. Define the types of transactions to include in the database. Since eCommerce fraud happens only with credit cards, exclude orders where creating a chargeback is impossible
3. Identify the patterns of fraudulent orders to differentiate between the good and the bad transactions
4. Model the data to teach the algorithm to define suspicious orders based on the patterns. This is where data intelligence professionals and/or tools perform approaches like deep learning algorithms
5. Implement the model
6. Add new fraud patterns to the algorithm.

As a result, it would be possible to reduce the amount of fraudulent orders by rejecting them and prohibiting fraudsters from making purchases.

What Are Some Examples of Fraud Patterns in eCommerce?

Here are some common examples of fraud patterns in eCommerce:

1. Account Takeover (ATO): Fraudsters gain unauthorized access to customer accounts by stealing login credentials through techniques like phishing, credential stuffing, or malware. Once they have control of an account, they can make unauthorized purchases, change shipping addresses, or redeem stored payment methods.

2. Payment Fraud: Fraudsters use stolen credit card information or other payment credentials to make fraudulent purchases. This can involve card-not-present (CNP) transactions, where the fraudster uses stolen card details to make purchases online without the physical card, or identity theft, where the fraudster impersonates a legitimate customer to open accounts or apply for credit.

3. Friendly Fraud: Also known as "chargeback fraud," this occurs when a customer makes a legitimate purchase but later disputes the charge with their credit card issuer, claiming that the transaction was unauthorized or that the goods were not received. In some cases, customers may falsely claim that an item was damaged or not as described to obtain a refund or replacement.

4. Phishing and Spoofing: Fraudsters send phishing emails or create fake websites that mimic legitimate eCommerce platforms to trick customers into divulging personal information, such as login credentials, payment details, or account information. Phishing attacks may also target employees of eCommerce companies to gain access to sensitive data or internal systems.

5. Account Creation Fraud: Fraudsters create fake accounts using stolen or synthetic identities to exploit promotional offers, discounts, or loyalty programs. They may also use these accounts to engage in other fraudulent activities, such as unauthorized purchases or account takeovers.

6. Inventory Manipulation: Fraudsters exploit vulnerabilities in inventory management systems to manipulate product availability, pricing, or inventory levels. This may involve listing counterfeit or nonexistent items for sale, falsely inflating inventory counts, or exploiting glitches in pricing algorithms to obtain goods at lower prices.

7. Return Fraud: Fraudsters exploit lenient return policies or loopholes in the returns process to return stolen or counterfeit merchandise for refunds or store credit. This may involve returning used or damaged items, swapping items for cheaper alternatives, or exploiting loopholes in return authorization procedures.

8. Bot Attacks: Fraudsters use automated bots to conduct malicious activities, such as account takeover attempts, credential stuffing attacks, or inventory scraping. These bots can overwhelm eCommerce websites with fake traffic, exhaust server resources, and disrupt legitimate user activity, leading to downtime or performance issues.

9. Transaction Laundering: Fraudsters use legitimate eCommerce transactions to launder money or facilitate other illegal activities. They may set up fake storefronts or shell companies to process payments for illicit goods or services, disguising the true nature of the transactions to evade detection by authorities or payment processors.

10. Dropshipping Fraud: Fraudsters exploit dropshipping arrangements to defraud merchants by placing large orders for goods using stolen payment credentials and then redirecting shipments to alternate addresses or reshipping them to other locations for profit.

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Data Analytics Provides Real Benefits

Every eCommerce business has tons of customer data they now can take advantage of with approaches like big data analysis and business intelligence analysis.

As you can see, there are at least 5 ways you can benefit from data analytics and business intelligence, and they can help with achieving a competitive advantage. With so many retailers - especially the big ones - taking advantage of them as we speak, it's safe to assume that data analytics will soon become a must in eCommerce.