Afterpay reduces fraud risk with advanced machine learning
Buy now, pay later (BNPL) business Afterpay is using the Cloudera Data Platform (CDP) to run streaming analytics and advanced machine learning for real-time fraud detection.
Afterpay has 3.6 million customers and increased its daily transactions from 100 million to 800 million in under a year, prompting a need to identify, access and reduce business risk by identifying duplicates and anomalies in those transactions.
Harnessing analytics capabilities on CDP Public Cloud, Afterpay’s engineers are now able to produce up to 800 million hybrid data streaming events and batch features for the data analysis team to perform fraud and predictive analytics for more data-driven and accurate insights.
Pinjie Huang, Engineering Manager, Afterpay, said, “Fraudsters today are becoming increasingly smarter in coming up with new patterns to take advantage of the system. We needed fast and flexible solutions to quickly adjust to newly identified fraud patterns or behaviours.
“The Cloudera Data Platform provided us with the right set of tools and visibility to proactively address these advanced fraud patterns by detecting issues and irregularities in real time. As a result, operational efficiencies improved by 30% and internal net transaction loss (NTL) were kept under 10%.”
Afterpay further enhanced the fraud detection program by employing Cloudera’s advanced machine learning capabilities. Engineers can now anticipate fraud activities by finding sequence patterns in valid transactions and flagging irregular transactions as potentially fraudulent. This resulted in a successful fraud rate reduction of 5%.
According to Keir Garrett, Vice President, Cloudera Australia and New Zealand, “Financial crime prevention requires continuous diligence and advanced analytics capabilities that accelerate detection and resolution.
“Given the surge in fraud activities, service providers need to up the ante in stemming BNPL fraud to give merchants and customers peace of mind over the security of their personal data.”
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