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.

Read how InetSoft was rated #1 for user adoption in G2 Crowd's user survey-based index.

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.

View a 2-minute demonstration of InetSoft's easy, agile, and robust BI software.

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.