Bias - Variance Tradeoff and Class Imbalance in Finance

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:

• Transaction amount
• Merchant category for the transaction
• Zip code for the merchant
• Zip code for the billing address
• Average transaction amount for the account over the past six months

Output feature:

• 0/1 flag for fraud (0 = not fraud, 1 = fraud)

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”).