Using Date-Time Stamp for Real-Time Fraud Prediction
Question
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?
Answers
Explanations
Click on the arrows to vote for the correct answer
A. B. C. D.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.
The correct answer is 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.
Explanation: The date-time stamp is a valuable feature in fraud detection since it can help identify patterns and anomalies over time. However, as mentioned in the question, the raw date-time stamp is unique and not very useful in its current form. Therefore, we need to transform it into meaningful features that our model can use.
One common technique is to extract the hour of the day, the day of the week, and the month from the date-time stamp. This transformation can help our model identify patterns and anomalies that occur at specific times of the day, week, or month.
For example, fraudsters may be more active during weekends or holidays, and our model should be able to identify this pattern. Alternatively, certain merchants or locations may be more prone to fraudulent activities at specific times of the day, and our model should be able to identify this as well.
Overall, transforming the date-time stamp into meaningful features can greatly improve our fraud detection model's performance and help identify patterns and anomalies that may be otherwise missed.