In the payments industry, machine learning is similarly becoming an increasingly important tool to help businesses combat fraud. As new technologies transform the way we pay — originally with credit and debit cards and, more recently, with kiosks, smartphones and other mobile devices — the number of transactions flowing through global payment networks has increased. At the same time, criminals have grown more sophisticated and more adept at using technology — even big data and analytics — to disguise illicit activity. As a result, it is getting harder and harder for businesses to determine which transactions to approve and which ones to reject.

Fraudulent activities in finance can be detected by looking at on-surface and evident signals. Unusually, large transactions or the ones that happen in atypical locations obviously deserve additional verification. Purely rule-based systems entail using algorithms that perform several fraud detection scenarios, manually written by fraud analysts. Today, legacy systems apply about 300 different rules on average to approve a transaction. That’s why rule-based systems remain too straightforward. They require adding/adjusting scenarios manually and can hardly detect implicit correlations. On top of that, rule-based systems often use legacy software that can hardly process the real-time data streams that are critical for the digital space.

However, there are also subtle and hidden events in user behavior that may not be evident but still signal possible fraud. Machine learning allows for creating algorithms that process large datasets with many variables and help find these hidden correlations between user behavior and the likelihood of fraudulent actions. Another strength of machine learning systems compared to rule-based ones is faster data processing and less manual work. For example, smart algorithms fit well with behavior analytics for helping reduce the number of verification steps.

There is no single machine learning algorithm or method that works. Success comes from the ability to try lots of different machine learning-based methods, trying variations on them and testing them with a variety of data sets. The data scientist needs a toolkit with a variety of supervised and unsupervised methods – as well as a variety of feature engineering techniques. Finally, there is a creative aspect or “art” to machine learning for fraud detection. It’s the application of machine learning in new and novel ways, like combining a variety of supervised and unsupervised methods in one system to be more effective than any single method alone.

Machine learning enables:

  • Real-time decisions: Advances with in-memory, event streaming technology allow risk scoring and decision making in the sub-second range (i.e., ultra-low latency).
  • Big Data set processing: Advances in distributed data processing allow analyzing more data while still maintaining real-time decisions without trade-offs between data and latency.
  • Reduced cycle time: Learning cycles are continuous unlike batch learning where models become out-of-date; With machine learning, the same transactions being scored also update/teach the machine learning models.
  • Increased effectiveness: Extremely subtle patterns and variations can be detected and delivered (e.g. precision, recall) better than humans in many tasks.
  • Error-free processing: Enormous amounts of data can now be processed without human-bias or error.
  • Cost efficiencies.

When used successfully, machine learning removes the heavy burden of data analysis from your fraud detection team. The results help the team with investigation, insights, and reporting. Machine learning doesn’t replace the fraud analyst team but gives them the ability to reduce the time spent on manual reviews and data analysis. This means analysts can focus on the most urgent cases and assess alerts faster with more accuracy, and also reduce the number of genuine customers declined.