Like all businesses, online retailers need to keep revenue high and costs low in order to stay profitable. Chargebacks – and the inadequate prevention of them – bleed profits in two ways. First, transactions which are approved and allowed to go through but later turn out to be fraudulent incur losses due to costly chargebacks (and the associated fees). Secondly, legitimate transactions which are erroneously declined decrease revenue, both for each single transaction, and possibly much longer as each customer impacted is then driven to seek a more frictionless shopping experience elsewhere.
With stakes this high, advances in artificial intelligence can provide a competitive edge, but in order to deliver the best results, machine learning algorithms need to be fed real-world data – and lots of it.
Feeding the Machine
In order for machine learning to work, the machine first needs data to learn from. Since both criminal and friendly fraud purchases are rare events, it’s a real challenge gathering enough examples of sketchy transactions for the system to develop, test and refine its model.
Riskified, a provider of ecommerce fraud prevention software was able to hook early adopters by their enticing chargeback guarantee. In exchange for data on all the transactions which they had already decided to decline, the firm offered to strain out any offers its system deemed good but was rejected by the merchant. Since only Riskified would be financially on the hook for the transactions it approved, the merchants had nothing to lose and some margin to gain. With a few of these merchants feeding Riskified’s system data, the supervised machine learning algorithm (based on deep learning) had enough legit and fraudulent transaction data to build its own filter.
This illustrates the key benefit of a SaaS/cloud provider for services like this: you benefit not only from your data, but also from the data of your provider’s other customers. This is especially true of machine learning based offerings, because more data means better decisions. It’s ultimately those decisions: the split-second judgment calls on every incoming transaction which determine either revenue or loss, satisfied customers or lost customers, beating back online criminals or siphoning money to them.
Continue reading at source: https://que.com/terminating-costly-fraud-the-rise-of-machine-learning/
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@Yehey
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