Fraud Detection Model Testing

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Questions and Answers

What is the main challenge in evaluating the production precision recall of the model?

Inability to train the model on recent data

Difficulty in observing the outcome of blocked charges (correct)

Uncertainty about the features used in the model

Lack of available data for model evaluation

Why is it difficult to answer questions related to production precision recall and model evaluation?

Lack of model features

Limited data for policy evaluation

Uncertainty about the business complaints

Inability to observe the outcomes of blocked charges (correct)

What is the main issue when retraining the model after a year?

Inability to train the model on recent data

Change in model features

Significant drop in model performance (correct)

Lack of validation data

According to the context, what percentage of scores are saved between zero and one on a 100 point scale?

Over 99% Signup and view all the answers

Why is there a transition where the score is constantly one until it hits 50 and then starts dropping off?

Because for scores below 50, the outcome has already been observed and no additional information is gained. Signup and view all the answers

In the context, what is the reason for observing a smoother transition on one side?

Observing the outcome for scores below 50 provides no additional information. Signup and view all the answers

What does the speaker mean by 'we have models for both things by dollar volume and just by count' in the context?

There are different models based on dollar volume and count for evaluating outcomes. Signup and view all the answers

What does the speaker imply by 'we're letting through way more things that have a score of 51 than have a score of 100'?

The system allows more items with a score of 51 to pass through than those with a score of 100. Signup and view all the answers

What was the main reason for the terrible performance of the new fraud detection model?

The model was trained on the hardest, uncaught fraud from the previous model Signup and view all the answers

Why was it suggested to run both models in parallel?

To catch both base fraud and new, harder residual fraud Signup and view all the answers

What was the challenge in evaluating the performance of the ensemble model?

The lack of labels for the charges Signup and view all the answers

How was precision proposed to be computed?

By asking what fraction of charges ended up being disputed Signup and view all the answers

How was recall proposed to be estimated?

By calculating the total number of fraud charges among charges below the threshold Signup and view all the answers

What was suggested to estimate the distribution and evaluate models?

Allowing a holdout set of charges to go through instead of being blocked Signup and view all the answers

How can the total amount of fraud caught be calculated?

By scaling the observed fraud by a factor of the number of charges let through Signup and view all the answers

What is the primary focus of the machine learning team at Stripe?

Detection and prevention of fraud Signup and view all the answers

How does Stripe's charging process involve tokenization?

The browser interacts with Stripe and receives a token for charging Signup and view all the answers

Why is delay in detecting fraud a concern for Stripe?

Credit card statements close monthly, leading to delayed reporting of fraud Signup and view all the answers

What data was used for training the machine learning model at Stripe?

Data from January 1st to September 30th Signup and view all the answers

How is precision defined in evaluating the performance of the machine learning model?

The fraction of charges the model flags as fraud that are actually fraud Signup and view all the answers

What type of features were used in building the machine learning model for fraud detection at Stripe?

Client IP country, card issuing country, and card number history Signup and view all the answers

Why are charge-backs a concern for merchants using Stripe?

Merchants lose funds and face penalties Signup and view all the answers

How does Stripe use rich information from the tokenization process in machine learning models?

To catch fraudulent activities Signup and view all the answers

What is the purpose of using precision and recall to evaluate model performance?

To understand both false positives and false negatives in fraud detection Signup and view all the answers

When was the machine learning model built at Stripe for fraud detection?

'2013' Signup and view all the answers

What is the consequence of delayed reporting of fraud at Stripe?

'Increased charge-back rates' Signup and view all the answers

What is the main concern associated with credit card statements closing monthly?

'Fraud may not be reported until 2-3 months later' Signup and view all the answers

What percentage of cases were recalled based on the identified and uncaught fraud?

89% Signup and view all the answers

How does the company compute precision and recall directly?

By allowing 5% of charges to pass through Signup and view all the answers

What factor is used to weight samples based on whether they were blocked or passed through in training?

20 Signup and view all the answers

What kind of charges is the company considering to only block under the new policy?

Charges that are certain to be fraudulent Signup and view all the answers

Which type of reports is more likely to be received by the company?

Reports of false positives Signup and view all the answers

What should be included in the ROC curve in model evaluation?

Weights for the model's predictions Signup and view all the answers

What is the threshold for blocking charges in the current setup?

50 Signup and view all the answers

What is the company evaluating while considering the cost of allowing more fraud to occur?

Model's precision and recall Signup and view all the answers

Study Notes

There are 80,000 cases of identified fraud and 10,000 cases of uncaught fraud, resulting in a recall of 80,000 out of 90,000 cases or 89%.
The company is allowing 5% of charges to pass through, which can be used to compute precision and recall directly.
In training, the company uses a 5% holdout and weights samples based on whether they were blocked or passed through by a factor of 20.
The company is considering a policy to only block charges that it's certain are fraudulent and allow false positives to be reported by businesses.
There are methods for businesses to report false positives and false negatives, but false positives are more likely to be reported.
The ROC curve in model evaluation should include weights for the model's predictions.
The current setup allows all charges with scores below 50 to pass through and blocks those with scores above 50.
The business has to consider the cost of allowing more fraud to occur while evaluating models.
Instead of a step function, a smoother curve can be used by mapping the classifier score to a propensity score, representing the probability of allowing the charge to go through.
The total number of charges let through should remain the same, but more charges with lower scores and fewer with higher scores should be allowed.
The distribution of scores in multi-production should be considered.

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Description

Test your knowledge on testing fraud detection models in production and dealing with false positives. This quiz covers scenarios where a new model or block causes an increase in false positives, leading to potential issues with blocking legitimate charges.