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Soups Ranjan

Head of Financial Crime Risk, Revolut

Soups Ranjan heads Financial Crime Risk at Revolut, the fastest growing challenger bank in Europe. He leads the team in charge of preventing financial crime on Revolut’s platform using data science and machine learning. Soups has 14 years of experience applying machine learning to domains ranging from network security to advertising and cryptocurrencies. Prior to Revolut, Soups was the director of data science and risk at Coinbase, one of the largest cryptocurrency exchanges in the world. At Coinbase, Soups built many engineering teams from the ground up including data, risk and identity. Soups is the co-founder of RiskSalon.org, a roundtable forum for risk professionals in San Francisco and Seattle to share ideas on stopping financial crime. Soups holds a PhD in ECE focused on network security from Rice University. Soups currently lives in Berkeley with his family.

Soups Ranjan

Sessions / 2019 / 1 talk

  • Data Security and Privacy in the Age of Machine Learning

    Personally Identifiable Information (PII) is piling up in databases and on filesystems across the globe. Smart companies are hard at work generating insights from this data, while World-dominating companies are intentionally generating it, mining it and in various ways obtaining clear value from it. GDPR and HIPPA are game-changing government regulations affecting data storage and transmission, while, in the meantime, advances in machine and deep Learning are powering huge leaps in analytical insights and business innovation. In addition, an unlevel playing field exists between the sheer size of the data accumulated at the biggest tech cos vs. the nimbleness and inspiration of the smallest startups. Yes, companies of all sizes are competing with each other in an attempt to add significant value to their users. At best, large datasets represent the bedrock for meaningful consumer insights; value-added customer features, services, and products; and massive amounts of rich training data to increase model efficiency. At worst, new systems, algorithms and data architectures represent a plethora of nefarious new opportunities to de-anonymize, leak or blatantly distribute data that was previously secret and/or obfuscated. So in this brave new world of data and algos and regulations what are the privacy concerns surrounding data access and security? Our panelists will explore these issues, from the hands-on perspective of building some of the most sophisticated data mining systems in the world. They are all hands-on technologists - data scientists, engineers, researchers and technical founders - and will share from their deep experience in building massively scalable data systems. They will also help us contemplate the thorny issues of technical and ethical responsibility - issues essential to consider as we all work together to build the data-driven systems of the present, and the future.

Sessions / 2017 / 1 talk

  • Payment Fraud in Digital Currency

    Coinbase is the one of the largest digital currency exchanges in the world. We store about $1B of digital currency (bitcoin, litecoin, ether) on behalf of our users. Given the instant nature of digital currency and that it can't be revoked, we have one of the hardest payment fraud and security problems in the world. We are hit by the most sophisticated scammers constantly and consequently we are at the forefront of the fight against fraud. We've witnessed and solved loopholes exploited by fraudsters years ahead of the broader industry (e.g., vulnerabilities in second-factor tokens delivered by SMS, phone porting attacks, loopholes in online identity verification, etc.). In this talk, I'll present examples of scammer trends and techniques we've seen through the past years. I'll also talk about our risk program that relies on rules-based systems, supervised and unsupervised machine learning as well as highly-skilled human fraud fighters.

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