Stripe: Radar Technical Guide

The recent, massive acceleration in e-commerce has created a corresponding increase in online payments fraud. Worldwide, fraud costs businesses more than an estimated $20 billion annually. Plus, for every dollar lost to fraud, the total cost to businesses is actually much higher due to increased operational costs, network fees and customer churn.Not only is fraud expensive, but sophisticated fraudsters are constantly finding new ways to exploit weaknesses, making fraud challenging to combat. That's why we built Stripe Radar, a machine learning–based fraud prevention solution, fully integrated within the Stripe platform. Radar's machine learning leverages the data from hundreds of billions of dollars in payments processed across the Stripe network each year to accurately detect fraud and quickly adapt to the latest trends, enabling you to grow without increasing fraud.This guide introduces Stripe Radar and how we leverage the Stripe network to detect fraud, provides an overview of the machine learning techniques we use, explains how we think about the efficacy and performance of fraud detection systems and describes how other tools in the Radar suite can help businesses optimise their fraud performance.Introduction to online credit card fraudA payment is considered fraudulent when the cardholder does not authorise the charge. For example, if a fraudster makes a purchase using a stolen card number that hasn't been reported, it's possible the payment would be processed successfully. Then, when the cardholder discovers the fraudulent use of the card, he or she would question the payment with his or her bank by filing a dispute (also known as a "chargeback"). Businesses can challenge a chargeback by submitting evidence that shows the payment was valid. However, for card-not-present transactions, if the payment is deemed by networks to have been truly fraudulent, the cardholder will win and the business will be liable for the loss of goods and other fees.Historically, businesses have used brute-force rules to predict and block suspected fraudulent charges. However, hard-coded rules – for example, blocking all credit cards used abroad – may result in blocking many good transactions. Machine learning, on the other hand, can detect more nuanced patterns to help you maximise revenue. In machine learning parlance, a false negative is when the system misses something it is designed to detect – in this case, a fraudulent transaction. A false positive is when the system flags something it shouldn't have – for example, blocking a legitimate customer. Before we get into the details of machine learning, it's important to understand the trade-offs involved.With false negatives, businesses are often responsible for the original transaction amount plus chargeback fees (the cost associated with the bank reversing the card payment), higher network fees as a result of the dispute and higher operational costs from reviewing charges or fighting disputes. Plus, if you incur too many disputes, you could end up in a network chargeback monitoring programme, which can lead to higher costs or, in some cases, the inability to accept card payments. False positives, or false declines, are when a legitimate customer tries to make a purchase but is prevented from doing so. False declines can cause the business to take both a gross profit and reputational hit. In fact, in a recent survey, 33% of consumers said they wouldn't shop again at a business after a false decline.…

This excerpt is published under fair use for community discussion. Read the full article at Stripe.