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Credit Card Fraud Prevention

Credit Card Fraud Prevention software built on Machine Learning technologies enables more accurate fraud detection

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Card fraud is the no. 1 cause of identity theft

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22 billion

The number of payment cards globally. An average American has more than three credit cards

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$30 billion

Global annual losses from fraud generated by card-based payment systems

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$6.2 billion

Global penalties for AML failures in 2019 with an average of $143.5 million per fine

Main targets of credit card scams


Online merchants

Payment gateway providers

Payment processing companies

Credit card payment systems


How to prevent credit card fraud with Machine Learning?

Financial institutions and banks have to prevent financial losses and fraud at the earliest stage. Card fraud is the no. 1 cause of identity theft.

Almost 30 billion dollars were lost worldwide to card fraud. Modern credit card fraud prevention is expensive. Digital ID checks cost around $2 per document, companies spend millions on KYC and AML, and still, the number of fraudulent transactions is growing. Banks and financial institutions have been relying on passive measures to counteract fraud based on past breaches or fraud behaviour history, and only some have invested in pro-active or predictive fraud prevention.

Modern Machine Learning based Credit Card Fraud detection tools enable higher accuracy in transaction risk detection than rule-based systems. AI & ML technologies can consider many more data points, including the tiniest details of behavior patterns associated with a particular account. Less manual work for additional verification. Fewer false declines.

Benefits of Machine Learning Credit Card Fraud Prevention

Automated, Real-Time

Enables companies to immediately respond to deviations from the norm, potentially saving millions that would have been lost to fraud otherwise. Eliminates the delay between spotting the problem and resolving it.

Self-Learning Algorithms

Evolves automatically based on the insights acquired from newer incidents. As the database grows larger, the number of patterns identified also increases, leading to more accurate insights.

Higher accuracy of fraud detection

Compared to rule-based solutions, machine learning tools have higher precision and return more relevant results as they consider multiple additional factors.

Fewer false declines

False declines or false positives happen when a system identifies a legitimate transaction as suspicious and wrongly cancels it. ML algorithms significantly reduce this factor.

Less manual work

ML-driven systems filter out 99.9% of normal patterns, leaving only 0.1% of events for experts to verify.

Alerts & Dashboards

Graphical summaries, powerful dashboards, and real-time alerts are built into the system for better transaction monitoring and fraud prevention.

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