Stripe: Radar Technical Guide
The rise in e-commerce has led to an increase in online payment fraud, costing businesses over $20 billion annually. Stripe has developed Radar, a machine learning-based solution to combat this fraud by leveraging extensive payment data. The guide discusses the challenges of fraud detection, including the balance between false positives and false negatives in transaction processing.
- ▪Fraud costs businesses worldwide more than an estimated $20 billion annually.
- ▪Stripe Radar uses machine learning to detect fraud and adapt to new trends in payments.
- ▪Businesses face challenges in balancing the prevention of fraud with the risk of blocking legitimate transactions.
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The recent, massive acceleration in e-commerce has created a corresponding increase in online payments fraud. Worldwide, fraud costs businesses more than an estimated $20 billion annually. Plus, for every dollar lost to fraud, the total cost to businesses is actually much higher due to increased operational costs, network fees and customer churn.Not only is fraud expensive, but sophisticated fraudsters are constantly finding new ways to exploit weaknesses, making fraud challenging to combat. That's why we built Stripe Radar, a machine learning–based fraud prevention solution, fully integrated within the Stripe platform.
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Excerpt limited to ~120 words for fair-use compliance. The full article is at Stripe.
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