Fraudulent credit card transactions are really in many transactions. We can apply machine learning algorithms to lies the past data and predict the possibility of a transaction being a fraud transaction.

Overview Of E-Commerce Fraud Detection

  • Buying and selling of goods or services using the internet, and the transfer of money and data to execute these transactions. ​
  • E-Commerce is undeniably one of the biggest sectors in online business. ​
  • And therefore, Fraud in e-commerce has substantially increased globally over the last few years.​

Types of Fraud in E-Commerce​

Buyer fraud

  • Credit Card Fraud ​
  • Reseller Fraud ​
  • Product Exchange Fraud ​

Seller fraud

  • Reviews/Ratings Fraud​
  • Fake Listing ​
  • Price Abuse (MRP abuse) ​

Credit Card Fraud Detection​

Credit card fraud is a form of identity theft that involves an unauthorized taking of another's credit card information for the purpose of charging purchases to the account or removing funds from it.

Problem Statement​

The Credit Card Fraud Detection Problem includes modeling past credit card transactions with the knowledge of the ones that turned out to be fraud. This model is then used to identify whether a new transaction is fraudulent or not. Our aim here is to detect fraudulent transactions while minimizing the incorrect fraud classifications.​

Import Libraries

Loading the Dataset​

Summary of Data

Handle the data

Checking for null values

Build Model

  1. Split the datasets in training and testing data

2.  Train the model using XGB Classifier

3.  Test or predict the model using the test data

4. Check Accuracy

Conclusion

Fraud detection is a complex issue that requires a substantial amount of planning before throwing machine learning algorithms at it. Nonetheless, it is also an application of data science and machine learning for the good, which makes sure that the customer’s money is safe and not easily tampered with.