Credit Card Fraudulent Transactions

DataRobot predicts which credit card transactions are most likely fraudulent based on transaction characteristics.

The cost of credit card fraud is billions of dollars per year. By accurately predicting which transactions are likely fraudulent, banks can significantly reduce these illegal transactions while providing card holders an excellent customer experience.


Fraudulent transactions are costly, but it is too expensive and inefficient to investigate every transaction for fraud. Even if possible, investigating innocent customers could prove to be a very poor customer experience, leading some clients to leave the business.


Using DataRobot, you can automatically build extremely accurate predictive models to identify and prioritize likely fraudulent activity. Fraud units can then create a data-based queue, investigating only those incidents likely to require it. The resulting benefits are two-fold. First, your resources are deployed where you will see the greatest return on your investigative investment. Additionally, you optimize customer satisfaction by protecting their accounts and not challenging innocent transactions.

Why DataRobot

Automate building an accurate model to predict the likelihood that a financial transaction is fraudulent. With the results you can create a rank-ordered queue of transactions for fraud units to investigate.

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