What challenges did Seeking.com face before AI implementation?

Mitigating sophisticated fraudulent accounts and bad actors on an online dating platform. Ensuring user trust while maintaining a smooth app experience without drop-off after enhanced verification steps.

What was the impact of the AI solution?

Reduced fraudulent accounts by over 90%. Enhanced user trust and security standards on Seeking.com. Maintained high user engagement with virtually no drop-off after adding verification steps.

Source

Evidence: Medium50/100

Seeking.com Reduces Fraudulent Accounts by Over 90% Using Amazon Rekognition Face Liveness

Use case typeFraud detectionUpdated Jun 13, 2026

Seeking.com, a luxury dating platform, faced challenges in mitigating fraudulent accounts and bad actors to provide a trustworthy, secure experience for its users. The company implemented Amazon Rekognition Face Liveness to detect real users and deter spoofs in seconds during facial verification, effectively spotting face masks, printed photos, and deepfakes. Additionally, Seeking.com utilized Amazon Bedrock to identify suspicious behavior patterns, ensuring continuous fraud prevention beyond initial screening.

Organization: Seeking.com
Industry: Tech & Comms
Location: United States
Published: April 2026

Reported outcomes

−90%

quantified impactOther quantified impact

Strategic outcomes

Risk & complianceReduced fraudulent accountsCustomer experience & trustEnhanced user trust and securityCustomer experience & trustMaintained high user engagementNew product / capabilityAdded real-time facial verification

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

Normalized claim

Quantified impact: 90% decrease

AWS Customer StoriesApr 29, 2026Customer storyInferred claimMedium evidence strength

Reduced fraudulent accounts by over 90%.

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

Customer: Seeking.com
Provider: AWS
Maturity: Unknown
Linked source: AWS Customer Stories

No explicit deployment-stage evidence found.

Customer identity supportedSource describes one deploymentMaturity evidence evaluated

Primary read

Use case focus

Showing 3 of 3

1Fraud Detection
2User Verification
3Security

Mitigating sophisticated fraudulent accounts and bad actors on an online dating platform.
Ensuring user trust while maintaining a smooth app experience without drop-off after enhanced verification steps.

Implemented Amazon Rekognition Face Liveness for real-time facial verification to detect spoofs.
Used Amazon Bedrock for generative AI to analyze and identify suspicious behavior patterns post initial verification.
Integrated verification as a required step during user onboarding to reduce fraud effectively.

Technologies

Amazon Rekognition Face Liveness Amazon Bedrock

Reduced fraudulent accounts by over 90%.
Enhanced user trust and security standards on Seeking.com.
Maintained high user engagement with virtually no drop-off after adding verification steps.

Sources & evidence1

Evidence: Medium50/100Evidence strength