Accelerating On-Demand Business With Trust

How Cabify Keeps Fraud Down and Business Booming

On-demand businesses have flourished across a variety of industries and geographies, but face many unique challenges as fraudsters are attracted to their quick turnaround times. This webinar will explore the unique challenges facing on-demand businesses with insights from Jorge Garcia and Armando Rivas of Cabify, a leading ride-sharing company based in Madrid.

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In this webinar, you’ll learn:

  • The unique and varied challenges facing on-demand businesses
  • How to expand successfully into new geographies while keeping fraud down

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Kevin Lee – Panelist

Trust and Safety Architect, Sift Science

Kevin Lee is driven by building high performing teams and systems to combat malicious behavior. He has worked for the last 10+ years around developing strategies, tools and teams responsible for billions of users and dollars of revenue. Prior to Sift Science, Kevin worked as a manager at Facebook, Square and Google where he lead various risk, chargeback, spam and trust and safety organizations.

Jorge Garza – Panelist

Head of Fraud and Payments, Cabify

Jorge Garza is an experienced fraud and payments strategy manager with 10+ years of experience managing large projects and operational teams globally. He has broad experience in eCommerce, credit card fraud prevention and payments operations, credit ops, IT project management, shared services and customer fulfillment operations.

Armando Rivas – Panelist

Fraud Manager, Cabify

Armando Rivas has a wide experience in Risk Control and Fraud Prevention. His background includes Issuer, Acquirer and Merchant experience. Prior to Cabify he has worked for American Express, Northgate and Santander-Elavon.

Thousands of sites and apps build trust with Sift

Open Table reduced manual review by 80%. 200% improvement in detection accuracy.
60–70% reduction in spam content, more frictionless environments for valued users.
Entropay increased user conversion rates by 15% and now dedicates 0 full-time employees to fraud.