How to Build AI You Can Trust, With Adyen's Dhruv Ghulati
In a Nutshell
Dhruv Ghulati emphasizes building trustworthy AI through deep fraud pattern understanding, human-in-the-loop systems, and governance frameworks that balance performance with explainability. Key infrastructure investments include sandbox environments and democratized data access to enable rapid experimentation rather than pre-planned use cases. The core message: invest in accessible AI tooling and feedback loops while maintaining interpretability safeguards, as the current era rewards hands-on creative building over rigid planning.
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Dhruv Ghulati started his career in finance and banking, with his first job at Merrill Lynch on the trading floor. Around 2014, he became interested in technology and entered AI. His career path included being an early-stage founder in London, working on a company that detected misinformation and disinformation online. After exiting his first company, he focused on developing AI product management skills, recognizing that understanding product is essential for technical leadership and entrepreneurship. He moved to Amsterdam where he led the Uber AI team on product as the first site lead, working on AI problems across mobility, Uber Eats, delivery, and forecasting.
Dhruv currently serves as a principal product manager at Adyen, leading AI product development for payments. His career has consistently focused on detecting fraud and negative content, from identifying misinformation in his startup to detecting counterfeits and forged documents at Onfido, and now working on chargeback fraud detection at Adyen.
Effective fraud detection requires understanding how fraudulent information is generated. This involves going deep into fraud patterns, including how generative AI creates disinformation, the motivation of attackers and creators, and the techniques they deploy. Without this understanding, building effective detection systems is impossible. The process operates as a continuous cat-and-mouse game, with waves of creation followed by regulation, moderation, and safety measures.
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