This episode features a candid conversation between André Ferraz, Incognia’s CEO, and Rizwan Shaikh, VP of Customer Service Delivery at Rapido — one the biggest mobility platforms in India.

They unpack one of the big challenges of the gig economy: maintaining a healthy driver supply while also keeping risky drivers off the platform.

Discussion topics include:

• Rapido’s innovative SaaS-style model for drivers (no per-ride fees)
• When to warn a driver vs. when to ban — and giving banned drivers a chance to reactivate themselves
• The importance of VOC — from both riders and drivers
• Scaling to 40%+ automated onboarding with zero human touch

Key takeaways:

• Platform integrity starts with rider feedback. Rapido turns real-time rider input into actionable fraud signals — using VOC to trigger selfie verification, detect mismatches, and prioritize platform safety without adding friction.
• Enforcement isn’t one-size-fits-all. Rapido applies a severity-based approach to fraud: high-risk violations (like impersonation) lead to instant bans, while lower-risk behaviors (like fare manipulation) may result in warnings and education — helping retain supply without compromising trust.
• Automation is scaling fraud prevention and onboarding. With over 40% of driver onboarding now automated (and goals to increase that number to 80%), Rapido is proving that fast, frictionless onboarding and fraud controls aren’t mutually exclusive — they’re both essential to growing a safe gig economy platform.

Show notes:

Connect with Rizwan: https://www.linkedin.com/in/rizshaikh01/

Connect with Andre: https://www.linkedin.com/in/andreferraz/

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Promo abuse is silently siphoning millions from gig economy platforms—hurting margins, growth metrics, and customer acquisition efforts.

This episode is a discussion between Sudhir Lanka (Grubhub), Danielle Miguez (Zé Delivery), and Jeniffer Rosa (Incognia) about how promo fraud happens, what to watch for, and how to stop it without killing your growth engine.

Discussion topics include:

• How promo abuse really works—from fake restaurants to fake accounts and device farms
• How to recognize the early warning signs of abuse and uncover vulnerabilities
• How to distinguish between clever users and real fraud
• Proven strategies for cracking down on abuse without killing growth
• How fraud teams can partner with marketing to stop abuse and support sustainable growth

Key takeaways:

• Promo abuse is silent but costly. It often goes undetected because it mimics normal user behavior—until fraud teams spot patterns like dozens or hundreds of accounts linked to a single device.
• Fraudsters are scaling. Professional fraud rings can use app cloners, emulators, synthetic identities, and GenAI to create and manage massive networks of fake accounts.
• Not all abuse is equal. There’s a spectrum—from opportunistic users exploiting a discount once or twice to organized fraudsters profiting at scale. Intent, deception, and scale help define the difference.
• Strong prevention signals and cross-team alignment are key. Device and location intelligence and tight coordination between fraud and marketing teams are essential to stop abuse without killing growth.

Show notes:

• Promo Abuse: The Silent Fraud Hiding Behind Your Best Campaigns: https://www.incognia.com/blog/promo-abuse-the-silent-fraud-hiding-behind-your-best-campaigns
• The Frontline Report Gig Economy Edition: https://www.incognia.com/frontline-report-gig-economy-edition#promotion-abuse
• Connect with Danielle: https://www.linkedin.com/in/danielle-miguez-517631129/
• Connect with Sudhir: https://www.linkedin.com/in/sudhirlanka/
• Connect with Jeniffer: https://www.linkedin.com/in/jeniffer-rosa-de-paula/

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What happens when a gig economy platform replaces gut instinct with data-driven trust scores?

In this episode, we sit down with Kunal Kumar, Principal Product Manager at Rebel Foods—a global cloud kitchen company in the food delivery industry—who led the design and rollout of trust scores for both customers and delivery riders. He shares how Rebel built their models, what signals matter most, and the lessons learned while balancing fraud prevention with customer experience.

Key discussion topics include:

• Why Rebel Foods decided to create trust scores in the first place
• How trust scores balance fraud detection with fairness
• Segmenting users into tiers and tailoring treatment flows
• Cross-team alignment and the implementation process
• Using metrics to monitor effectiveness
• Pitfalls to avoid when designing scoring models

Key takeaways:

• Data over instinct. Trust scores remove bias and subjectivity from fraud decisions, replacing gut calls with a consistent, data-driven framework.
• Segmentation & treatment. Customers and riders are divided into tiers, with high scorers getting perks like fast refunds and low scorers facing stricter checks.
• Impact without compromise. Rebel Foods reduced retention spend and doubled refund automation after launching trust scores—while keeping customer satisfaction steady.
• Built for trust. Trust scores were built to reward genuine behavior, with every user starting with a high score and only dropping if risky patterns appear.
• Location is critical. GPS fidelity is a critical rider signal that's used to improve customer ETAs, ensure fair pay for riders, and catch compensation abuse.

Show notes:

• See Incognia's other resources: https://www.incognia.com/resources
• Connect with Kunal: https://www.linkedin.com/in/kunal-kumar-26aa27a3/
• Connect with David: https://www.linkedin.com/in/david-nesbitt/

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