ou have been tasked to lead a project to detect fraudulent credit card transactions. You have worked with the data scientists and determined that the model that you choose should be able to identify these common anomalies in the system easily.

• If there are multiple payment methods added from a single account within an hour, then it is a trigger that this account may be fraudulent.
• If the customer is buying premium goods in large quantities, then your algorithm should be able to detect this fraud.
• The location or the address added to the profile is fraudulent; i.e., it does not exist.
• The email ID seems suspicious.
• There is a mismatch in the account name as well as the name of the card.

You have developed the model with the training set and have refined it with test data. You are ready to put it into production, but need to tell the story to your superiors before you put into action. What kind of data visualization would you use? Which quadrant does this belong in and why? What story would you want to tell them?