BlaBlaCar Case Study: Diversify your Machine Learning’s Capabilities to Different Fraud Schemes
As a Customer to Customer carpool platform, our main mission is to facilitate connections and exchanges between travelers willing to reach the same destination. Unfortunately, it is also an opportunity for fraudsters with different objectives and techniques to abuse our platform and hurt our business.
This presentation will show you how we have been able to leverage the same machine learning tool, initially built to fight non-payment fraud attacks (scams, hacked accounts etc) to detect money laundering networks operating on our platform, and then mitigate chargeback fraud as well.As a Customer to Customer carpool platform, our main mission is to facilitate connections and exchanges between travelers willing to reach the same destination.
Unfortunately, it is also an opportunity for fraudsters with different objectives and techniques to abuse our platform and hurt our business.
This presentation will show you how we have been able to leverage the same machine learning tool to address multiple types of bad actors operating at non-payment - hacked accounts, scams - and payment sides (money laundering).
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