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A/B Testing

Algorithms

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Probability

Product Metrics

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Statistics

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We are looking into creating a new partner card (think Starbucks chase credit card or Whole Foods chase credit card). You have access to all of our customer spending data.

How would you determine what our next partner card should be?

You work at Chase. We are looking into creating a new partner card (think Starbucks chase credit card or Whole Foods chase credit card).

What would be a reason why we would want to create partner cards?

You work at Chase. We are looking into creating a new partner card (think Starbucks chase credit card or Whole Foods chase credit card). 

What would be a reason why we would want to create partner cards?

Partner Card

New Partner Card

Business Problem Solving

Suppose there exists a new airline named Jetco that flies domestically across North America. Jetco recently had a study commissioned that tested the boarding time of every airline, and it came out that Jetco had the fastest average boarding times of any airline.

What factors could have biased this result, and what would you look into?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

What could potentially bias the average boarding time in favor of an airline that only flies domestically?

Boarding Times Bias

A new airline came out as the fastest average boarding times compared to other airlines.  What factors could have biased this result and what would you look into?

You’ve been tasked with building a classification machine-learning model to predict whether a transaction is either fraud or not fraud for a credit card company. You have ten years of historical data on transactions, including a flag for whether a transaction was manually identified as fraud.

Describe how you might go about building a fraud detection model for credit card transactions. Be sure to mention the possible model types, discuss the bias-variance tradeoff in model development, and address any complexities that arise from the class imbalance.

Input features in the data include:

<ul>
<li>Transaction amount</li>
<li>Merchant category for the transaction</li>
<li>Zip code for the merchant</li>
<li>Zip code for the billing address</li>
<li>Average transaction amount for the account over the past six months</li>
</ul>

Output feature:

<ul>
<li>0/1 flag for fraud (0 = not fraud, 1 = fraud)</li>
</ul>

Note: Fraudulent transactions are (thankfully) a very small percentage of all historical transactions. Assume fraudulent transactions are 0.01% of historical data.While building the model to perform the classification, you need to consider the bias/variance tradeoff, and take into account the fact that there is a class imbalance (very few of the observations are “fraud”).

Bias variance tradeoff and class imbalance in finance

Question	Topic	Difficulty	Ask Chance
New Partner Card	Business Problem Solving	Easy	Very High
Boarding Times Bias	Statistics	Medium	Medium
Bias - Variance Tradeoff and Class Imbalance in Finance	Machine Learning	Medium	Medium

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