Brought To You By

OPERATIONALIZING MACHINE LEARNING SYSTEMS FOR FRAUD Michael Manapat / @mlmanapat

Brought to you by

Brought To You By22

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?

Brought To You By33

Stripe

Lead development onmachine learningproducts

Brought To You By44

Fraud costs Stripe users in (at least) three ways

Brought To You By55

Let’s build a machine learning classifier for predicting fraud.

Brought To You By66

• 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)

Brought To You By77

Every choice represents a trade-off (recall ROC and precision-recall curves)

Brought To You By88

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

Brought To You By99

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

Brought To You By1010

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”

Brought To You By1111

What happened the second time we trained the model?

What’s hard about production monitoring?

Brought To You By1212

• 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

Brought To You By1313

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

Brought To You By1414

• 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”

Brought To You By1515

• 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! mlm@stripe.com / @mlmanapat