Machine Learning for Credit Card Fraud Detection

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Credit card companies must be able to recognize fraudulent credit card transactions so that customers are not charged for items that they did not purchase.

Using Artificial Intelligence to detect fraud helps businesses improve internal security and simplify corporate operations.

In this way, artificial intelligence has become a powerful tool to prevent financial crime due to its greater efficiency compared to other traditional methods.

This example teaches you how to build fraud detection models using your organization's data.

This example teaches you how to build fraud detection models using the data science and machine learning platform Neural Designer.

The data file used for this example comprises 3075 payments.

For each transaction, it includes the following information:

The data set includes the following variables:
-Average of the amount per transaction per day.
-Transaction amount.
-Whether the credit card is declined or not.
-The total number of declines per day.
-Whether it is a foreign transaction or not.
-Whether the transaction is in a high-risk country or not.
-Daily average of chargeback.
-6-months average of chargeback.
-Frequency of the 6-months chargeback.

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