Creating a Data-Driven Fraud Prevention Strategy for Your Business


    Purchase data can help drastically improve the effectiveness of your fraud prevention strategy. In this article, learn how to leverage your internal data to better protect your business from fraud.

    For those who are unaware, the eCommerce industry is projected to grow exponentially in the coming years. It’s estimated that consumers spent over $861 billion online with U.S. merchants alone in 2020, which is an increase of over 44% from 2019.

    However, this also means that online merchants need to prioritize security in different ways. Having a data-driven fraud prevention system is now table stakes for eCommerce businesses. 

    Leveraging internal transaction data should be the first step in improving the efficiency of your fraud prevention system. In this article, we'll look at how you can adopt a data-driven approach to fraud prevention in order to better protect your business from the threat of card-not-present (CNP) fraud.

    Learn about how to determine fraud risk based on: 

    Determining Risk by Product Type

    The capability to determine which products are at the highest risk of fraudulent purchase is essential when it comes to developing a comprehensive fraud prevention strategy. For instance, let’s assume that you sell handcrafted jewelry on your online store.

    When analyzing your purchase data, you identify a trend of chargeback claims coming from a particular line of products: 24K gold bracelets. It's clear that these products are high-risk since they consistently generate the greatest number of chargebacks for your business.

    As a response, you could then configure your risk threshold to be stricter on those items in order to mitigate the chance of a fraud scenario occurring. Adding additional authentication layers to the checkout process could be another successful way to prevent fraud for high-risk products. Over time, this approach will help you maximize your sales revenue and decrease fraud losses associated with chargeback remediation. 

    Determining Risk by Geographic Location

    Certain geographic locations are more prone to fraud than others. This will highly depend on multiple factors, such as your industry and the types of products that your business sells.

    However, it's highly inadvisable to blacklist an entire region, since you may miss out on a large number of legitimate sales.

    Instead, you can use transaction data to determine other criteria that contribute to fraud. For instance, you may find that a small segment of an international state is responsible for the majority of the fraud in that region.

    You can then adjust your risk threshold accordingly.

    Determining Risk Based on Seasonality

    It should come as no surprise to learn that instances of fraud grow exponentially during holiday seasons. In fact, fraud rates in some industries could grow as much as a few hundred percent.

    So, your risk threshold should adapt to this occurrence. In practice, this means that you need to be much more vigilant when it comes to detecting fraud near holidays, especially those that involve gifts (such as Christmas).

    However, it’s essential that you don’t make your risk threshold too heavy-handed. Otherwise, your customers may find it highly inconvenient to use your online platform and may even turn to competitors instead.

    You should aim to strike a balance that allows you to minimize the number of fraudulent transactions while avoiding causing complications for legitimate customers.


    Data-Driven Fraud Prevention is Essential

    The value of leveraging internal data to improve your fraud prevention strategy should not be underestimated. Using the above tactics, you should be better equipped to stop fraud on your eCommerce store and start increasing your transaction approval rate.

    Want to learn more about how Vesta can improve the effectiveness of your fraud prevention system? Feel free to request a demo and learn about how our transaction guarantee platform leverages 20+ years of consortium data to completely eliminate the cost of fraud.


    Other posts you might be interested in