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OPERATIONALIZING MACHINE LEARNING SYSTEMS FOR FRAUD Michael Manapat / @mlmanapat
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Outline• About Stripe• Consequences of fraud• Operationalizing a new model• How do we determine how to take action from model
outputs?• Ongoing monitoring of a released model• How do we reason about model performance when
our use of the model changes the world?
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• Fraud teams often don’t make disciplined decisions• Loss aversion• Concrete fraud loss vs. conversion loss• Structural bias of a fraud team
• Fraud prevention platforms/case studies will often emphasize a single metric (chargeback rate reduction, false positive rate)
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Break-even precision
1 fraudulent sale wipes out 19/6 (= 3.17) good salesBreak-even precision = 1 / (1 + 3.17) = 0.24
$10 Widget$4 COGS
$4 COGS$15 Chargeback fee$19 Total loss
Fraud
$6 Profit
Legitmate Sale
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Best Practices
• Formulate goals that incorporate tradeoffs• Reduce dispute rate?• Maximize revenue (with dispute rate cap?)
• Have a team own the goal and trade-off
• Run business process simulations
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So…• We’ve generated an ML model for fraud• Picked an operating point on the ROC curve
• We’d like to keep track of the precison-recall curve in “production”
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What happened the second time we trained the model?
What’s hard about production monitoring?
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• We are changing the state of the world by taking action with the model• Blocked charges never have a chance to
become fraud (or not)• Making a trade changes a price• etc.
• We need to estimate what the world would be like in the absence of the model
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Counterfactual evaluation
• Let through 5% of charges we would otherwise action with the blocking policy
• Weight those samples by a factor of 1 / 0.05 = 20
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• What if we have a fixed budget for this?
• Let’s get data on where we’re most uncertain about the outcome
• Important: we’re not “letting through fraud”
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• Formulate business goals for ML models that incorporate the precision-recall tradeoff, and have one team own the goal/trade-off
• Make sure you have a counterfactual evaluation plan before your model begins changing the world
Thank you! [email protected] / @mlmanapat