Secure your business from login to chargeback
Stop fraud, break down data silos, and lower friction with Sift.
- Achieve up to 285% ROI
- Increase user acceptance rates up to 99%
- Drop time spent on manual review up to 80%
By Stephanie Yee /
Updated
Ten-second summary: Prevent chargebacks (both criminal and friendly) with cross-functional coordination. To maximize profits, your fraud team should work with:
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Preventing e-commerce chargebacks — due to malicious purchases with stolen credit cards or ‘friendly-fraud’ from disgruntled customers — is a challenge that requires strategic coordination.
By working cross-functionally, your fraud team can:
Frictionless checkout is critical. An estimated 18% of customers who abandon carts do so because of checkout page complexity. ((The Baymard Institute’s E-Commerce Checkout Usability Report (June 2013) and blog has extensive research on ecommerce desktop and mobile best practices, like this post on streamlining the checkout process.)) Too simple a payment form, however, hinders chargeback prevention.
So how should your web design and fraud teams simplify checkout?
Rationalize fields. Some payment fields can be inferred from others and removed without impacting chargeback fraud prevention. For example, the customer’s billing address and zip code are critical to assessing chargeback risk, but billing city & state can be inferred from zip code. Similarly, credit card issuer maps to credit card number.
Apple’s payment form has low friction
Simplify field entry. Let shoppers input information as directly as possible. Expiration date fields and provide the same data to the fraud team, but the latter requires converting month number to name. Also, be mindful of the order of text fields and drop-down boxes, to reduce a customer’s switching between keyboard and mouse.
This payment form won’t prevent chargebacks — it’ll give you carpel tunnel!
Turbocharge chargeback prevention with data. Some fields are bottlenecks to conversion, but incrementally useful for fighting fraud. Remove the field, but keep chargebacks low by using signals derived from the customer themselves. Sift Science’s machine learning technology, for example, can surface subtle fraud signals based on a user’s behavioral, network and identity traits. ((Conventional rules-based fraud detection assumes the past is like the present, which isn’t the case with fraudsters. We’ll write more on this later, but data will only boost your bottom line if you have the right tools!))
Every business is different, so run A/B tests to optimize your unique conversion-information trade offs. Event tracking in Google Analytics can elucidate form abandonment issues on a field-by-field basis. (See footnote for info & alternatives). ((Be sure to use _trackPageview and be sure to set up a separate profile. For more about event tracking, see here. There are also jquery-based methods, simpler less granular tracking, and standalone software options.))
Friendly fraud accounts for 23% of revenue lost to chargebacks, but this does not include the 57% of all fraud losses from credits issued. ((Cybersource (2013) via the Fraud Practice)) Friendly fraud is exasperating but inevitable since it’s easy for consumers to contest charges.
Collaborate with the customer service team. Your fraud team must be efficient and thorough in chargeback disputes. When a customer disputes a transaction, the onus is on you to prove that you acted correctly. ((The Fair Credit Billing Act protects consumers (and their credit scores) form billing errors and requires that credit card companies address disputes in a certain amount of time. Effective at protecting consumers from potential abuse by credit card companies, an unfortunate side effect is that chargebacks become an easy and effective way for disgruntled ecommerce shoppers to express discontent.))
Fraud losses — whether malicious criminals or friendly — and are a challenge. With strategic collaboration, data, and machine learning, you can overcome them and boost your bottom line.
At Sift Science, we believe all merchants should have access to the same class of technology used by Google and Amazon to prevent e-commerce fraud at a reasonable price. Get started with Sift’s simple, one-hour integration!
Any other questions? We’d love to hear from you! Ping us at info@siftscience.com.
Stephanie Yee was a Director at Sift.
Stop fraud, break down data silos, and lower friction with Sift.