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Sellkite ‑ Fraud Filter logo

Sellkite ‑ Fraud Filter

Develop by Shopify
Free plan available

Block spam/fraud orders based on rules.

Create custom filters
Flag suspicious orders
Cancel fraudulent orders
Restrict specific consumers
The app is no longer available

About Sellkite ‑ Fraud Filter

Launched Mar 22, 2022

Categories

Built by Shopify

Zotasell: AI Upsell & Bundles logo
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Description

Create rules to flag or block known customers from ordering from your store.

Fraud Filter can help you fight fraud and minimize chargebacks by adding an extra degree of security. You may easily set up warnings for suspicious orders or totally prohibit orders using Fraud Filter's simple rule-based capabilities. When an order matches a filter you've defined, Fraud Filter will flag it as suspicious and either warn you or cancel it for you, depending on the action you've selected.

Fraud Filter can help you fight fraud and minimize chargebacks by adding an extra degree of security. You may easily set up warnings for suspicious orders or totally prohibit orders using Fraud Filter's simple rule-based capabilities. When an order matches a filter you've defined, Fraud Filter will flag it as suspicious and either warn you or cancel it for you, depending on the action you've selected. more
  • Create custom filters to fit your company's requirements.
  • On the order page, orders that fit your filters can be marked as cancelled.
  • Filters can be used to restrict specific consumers from placing orders on store.

Pros

  • Helps fight fraud and minimize chargebacks by adding an extra degree of security
  • Easily set up warnings for suspicious orders or totally prohibit orders using rule-based capabilities
  • Ability to create custom filters to fit specific company requirements
  • Warnings or cancellations are automated when an order matches defined filters

Cons

  • Rule-based system might require constant updates to stay effective against evolving fraud tactics
  • Potential for legitimate orders to be flagged or canceled if filters are too strict, which could impact customer satisfaction
  • Setting up effective filters may require a good understanding of typical order patterns and potential fraud indicators

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