How we reduced chargeback rates for a world-renowed luxury marketplace
We enabled increased conversion rates and lowered the overall chargeback rate and the amount of manually-reviewed traffic for a large fashion marketplace.
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We enabled increased conversion rates and lowered the overall chargeback rate and the amount of manually-reviewed traffic for a large fashion marketplace.
Industry:
EcommerceProduct type:
EnterpriseManual review rate lowered by
Overall chargeback rate reduced by
CB rates for some client’s brands lowered by
Our client is one of the leading global platforms for the luxury fashion industry. It connects customers in over 190 countries with over 1,200 of the world’s best brands, boutiques, and department stores. The company reached out to us with three issues, which had to be addressed on a global scale:
Industry: Ecommerce
Fraud type:
The growing amount of manual review traffic generated by other anti-fraud solutions providers resulted in large financial costs and unnecessarily burdened the company’s fraud management team.
Merchants need to detect the risk of as many chargebacks as possible if they don’t want to incur significant monetary losses, which happens typically when a chargeback is granted, and the merchant must bear the financial burden.
Thanks to our machine learning models backed by our proprietary Profiler, we decreased its chargeback and manual review rates for the client’s key markets in just under four months of full integration for their US, China, UK, and Brazil customer bases.
The fraud prevention solution we developed for the client is based on custom technologies, including in-depth user profiling, device fingerprinting, behavioral biometrics, and advanced machine learning. We helped to slash chargebacks and strengthen the rapid growth of the company while ensuring a seamless user experience for desktop and mobile customers. The accuracy of all recommendations is established by our proprietary Profiler, which offers unmatched depth and breadth of online user profiling capabilities (5,000+ data points, mostly non-declarative, proprietary data extraction methods regarding hardware, software, network, and behavioral contexts).
Take advantage of a ready-to-take or custom machine learning models for data of all sizes.
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Our Data Science team implemented two supervised machine learning models to detect chargebacks. One was built to detect the risk of chargebacks directly, and the second was created with the intention of replicating the results of the previous fraud prevention solution's decision-making process. The DS team found the errors in the previous model and iterated from there. As a result, we managed to slash the manual review rate by nearly 60% while increasing the percentage of accepted traffic in comparison to competitors’ rates of accepted traffic.
Our Data Science team also developed several models built on sensitive segments of traffic that were the most important for the client in order to detect fraudulent interactions and prevent them from becoming chargebacks. The overall chargeback rate decreased by over 12%. For the fashion ecommerce platform’s most valuable brand, the chargeback rate was lowered by as much as 89%.
Manual review rate lowered by
Overall chargeback rate reduced by
CB rates for some client’s brands lowered by
Industry: Ecommerce
Fraud type:
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