Credit Card Fraud Detection: How Machine Learning Can Protect Your Business From Scams

What is credit card fraud and who become targets of scams

  • cardholders,
  • online merchants,
  • payment gateway providers,
  • payment processing companies,
  • credit card payment systems,
  • card issuers (issuing banks), and
  • acquirers (acquiring banks).
Card-based payment process and its key players.

Identity theft

  • make purchases a cardholder doesn’t authorize,
  • withdraw money from a victim’s existing account (account takeover),
  • apply for a new credit card (fraudulent application), or
  • open a new account.

Transaction laundering

Transaction laundry involves selling prohibited goods via legitimate looking (or actually legitimate) websites. Resource: Sharetribe
  • ease of creation of a professional-looking website,
  • the large number of go-betweens involved in online payment processing, and
  • a rise in the use of corporate credit cards, enabling large payments to be done via a single transaction.

How machine learning helps with fraud detection

Key differences between rule-based and ML-based approaches to fraud detection.

Big players: what they do to protect online payments

How financial giants combat fraud with ML.

PayPal: taking a deep learning approach

Amazon: training models on AWS customers’ datasets

Visa: saving $25 billion a year with neural networks

  • a type of transaction (online, contactless, magnetic stripe, or in-app),
  • time of the day,
  • amount of money,
  • location, and
  • account’s spending patterns.

American Express: achieving the lowest fraud rate

Enhanced Authorization by AmEx helps merchants to effectively detect fraud.

Chase Merchant Services: proactively preventing threats

When and how to implement machine learning in fraud detection processes

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