You work for a financial services firm that wishes to enhance its fraud detection capabilities further. The firm has implemented fine-grained transaction logging for all transactions their customers make using their credit cards. The fraud prevention department would like to use this data to produce dashboards to give them insight into their customer’s transaction activity and provide real-time fraud prediction.
You plan to build a fraud detection model using the transaction observation data with Amazon SageMaker. Each transaction observation has a date-time stamp. In its raw form, the date-time stamp is not very useful in your prediction model since it is unique. Can you make use of the date-time stamp in your fraud prediction model, and if so, how?

Answer options:

A.No, you cannot use the date-time stamp since this data point will never occur again. Unique features like this will not help identify patterns in your data.
B.Yes, you can use the date-time stamp data point. You can just use feature selection to deselect the date-time stamp data point, thus dropping it from the learning process.
C.Yes, you can use the date-time stamp data point. You can transform the date-time stamp into features for the hour of the day, the day of the week, and the month.
D.No, you cannot use the date-time feature since there is no way to transform it into a unique data point.

Answer: C
Option A is incorrect since you can use the date-time stamp if you use feature engineering to transform the data point into a useful form.
Option B is incorrect since this option is really just another way of ignoring, thus not using, the date-time stamp data point.
Option C is correct. You can transform the data point using feature engineering and thus gain value from it for the learning process of your model. (See the AWS Machine Learning blog post: Simplify machine learning with XGBoost and Amazon SageMaker: https://aws.amazon.com/blogs/machine-learning/simplify-machine-learning-with-xgboost-and-amazon-sagemaker/)
Option D is incorrect since we can transform the data point into unique features that represent the hour of the day, the day of the week, and the month. These variables could be useful to learn if the fraudulent activity tends to happen at a particular hour, day of the week, or month.
Diagram:
Here is a screenshot from the AWS Machine Learning documentation depicting a typical fraud detection machine learning solution:
Reference:
Please see the Amazon Machine Learning developer documentation:
https://docs.aws.amazon.com/machine-learning/latest/dg/feature-processing.html.