What challenges did Artfinder face before AI implementation?

Handling large volumes of photos and videos manually was inefficient and frustrating for many customers across sectors. Manual processes for identity verification, fraud detection, content moderation, and media indexing were slow and error-prone.

What was the impact of the AI solution?

Reduced manual review processes by up to 40%, accelerating workflows and increasing accuracy for identity verification and content moderation. Enabled faster user identity verification with an 80% decrease in manual support tickets for verification issues. Improved customer experience by enabling fast photo identification and tagging, increasing sales and operational efficiency. Supported diverse industries with scalable, automated image and video analytics solutions.

Source

ProductionEvidence: Medium50/100

Amazon Rekognition Real-World Customer Use Cases

Use case typeFraud detectionUpdated Jun 13, 2026

Amazon Rekognition is used by organizations across multiple industries to add image and video analysis to applications. Use cases include facial recognition for customer verification, automated photo tagging, content moderation, event security, media indexing, remote test identity verification, fraud detection, marketing insights, and improving user experiences in mobile apps. Customers include Artfinder, CampSite, GeoSnapShot, Sen Corporation, Uluru, ZOZO Inc., Certipass, Aella Credit, Carbon, FanFight, Sportograf, C-SPAN, K-STAR Group, KYODO NEWS, Limbik, The Mainichi Newspapers Co., Make. TV, NFL Media, OSN, POPSUGAR, Scripps Networks Interactive, TheTake, Thorn, White House Historical Association, Utility, CoStar Group, Daniel Wellington, New Engen, Rekeep, Abode Systems, ARMED Inc., Marinus Analytics, Software Colombia, 3xLOGIC, Q5id, CA Mobile, FamilySearch, Go Girl Apps, Influential, Klear, Open Influence, Pattern89, Soul Platform, VidMob, Wia, Wisio, Woo, HERE Technologies, Mapillary, and Sygic.

Organization: Artfinder
Industry: Tech & Comms
Location: United States
Published: April 2026

Reported outcomes

−80%

timeTime & speed

−40%accuracy

Strategic outcomes

Speed & agilityAutomated manual review workflowsCustomer experience & trustImproved identity verification experienceCustomer experience & trustEnhanced photo identification and taggingScale & capacityEnabled scalable image analytics

Catalog median for time & speed deployments: −50% across 312 reported metrics. Compare benchmarks →

Why do we believe this?Outcome claims, sources, and evidence checks

Normalized claim

Accuracy: 40% decrease

Amazon Web ServicesApr 29, 2026UnknownInferred claimMedium evidence strength

Reduced manual review processes by up to 40%, accelerating workflows and increasing accuracy for identity verification and content moderation.

Normalized claim

Time: 80% decrease

Amazon Web ServicesApr 29, 2026UnknownInferred claimMedium evidence strength

Enabled faster user identity verification with an 80% decrease in manual support tickets for verification issues.

Why do we believe this deployment?Customer identity, provider attribution, maturity, and source checks

Customer: Artfinder, CampSite, GeoSnapShot, Sen Corporation, Uluru, ZOZO Inc., Certipass, Aella Credit, Carbon, FanFight, Sportograf, C-SPAN, K-STAR Group, KYODO NEWS, Limbik, The Mainichi Newspapers Co., Make.TV, NFL Media, OSN, POPSUGAR, Scripps Networks Interactive, TheTake, Thorn, White House Historical Association, Utility, CoStar Group, Daniel Wellington, New Engen, Rekeep, Abode Systems, ARMED Inc., Marinus Analytics, Software Colombia, 3xLOGIC, Q5id, CA Mobile, FamilySearch, Go Girl Apps, Influential, Klear, Open Influence, Pattern89, Soul Platform, VidMob, Wia, Wisio, Woo, HERE Technologies, Mapillary, Sygic
Provider: AWS
Maturity: Production
Linked source: Amazon Web Services

The service's Custom Labels enable automated brand and object tagging, enhancing marketing insights and operational efficiency

Customer identity supportedSource describes one deploymentMaturity supported

Primary read

Use case focus

Showing 3 of 4

1Facial Recognition
2Content Moderation
3Fraud Detection

Handling large volumes of photos and videos manually was inefficient and frustrating for many customers across sectors.
Manual processes for identity verification, fraud detection, content moderation, and media indexing were slow and error-prone.

Amazon Rekognition's image and facial recognition technology is integrated into customer platforms to automate photo tagging, identity verification, content moderation, and event security.
The service's Custom Labels enable automated brand and object tagging, enhancing marketing insights and operational efficiency.
Integration with AWS services allows scalable, accurate, real-time image and video analysis for various applications.

Technologies

Amazon Rekognition Amazon SageMaker

Reduced manual review processes by up to 40%, accelerating workflows and increasing accuracy for identity verification and content moderation.
Enabled faster user identity verification with an 80% decrease in manual support tickets for verification issues.
Improved customer experience by enabling fast photo identification and tagging, increasing sales and operational efficiency.
Supported diverse industries with scalable, automated image and video analytics solutions.

Sources & evidence1

Evidence: Medium50/100Evidence strength