How has Real-time Become a Mandatory Requirement in Fraud Detection?
By: Sophie Van Dessel
Tags: Machine Learning, Real Time Analytics, Fraud, Data Science, Fraud Detection

Fraud affects many industries, including banking and insurance as well as retail, healthcare, and law enforcement. Fraud is a high-cost threat to the integrity of any organisation. That is why companies invest massive amounts of money and efforts to protect themselves against fraudulent behaviours.

Because the methods of fraud evolve continually, it is essential for companies to act proactively. This means that companies should be able to prevent fraudulent activities before they take place. But how do you prevent and detect a fraud when malicious users use unconventional tactics? How do you identify the specific characteristics and phenomenon related to a specific fraud case? And more importantly, how do you ensure real-time detection?

Thanks to data science, fraud prevention technology has now made enormous strides. By applying algorithms that use historical data to detect anomalies and patterns, data scientists are able to discover a potentially fraudulent activity before it occurs… and act against it!

We at EURA NOVA have been able to leverage machine learning models and graphs to detect schemes and prevent fraud. Our R&D department has developed graph-based algorithms to identify fraudulent customers’ claims for an international insurance company. Graphs allowed us to effectively link different categories of data about customers and identify hidden social ties between them. By looking beyond individual data points, it was possible to uncover unknown and difficult-to-detect patterns. When these patterns were pinpointed, predicting and detecting fraud became a much more manageable task.

EURA NOVA has also been working for one of the largest actors in the banking sector to improve fraud detection. We built a model based on the business knowledge that fraudulent transactions have different characteristics comparing to the legitimate ones. Fraud detection algorithms were created based on these differences to predict the likelihood of a transaction being fraudulent before it is completed. For example, when a customer’s purchase made by a credit card occurs outside the normal circumstances or habits of the credit card owner, it is more likely that the transaction is fraudulent.

Our experiences have shown us fighting fraud requires to enrich the algorithms with real-time events. Our R&D director Sabri Skhiri says:

We have learned that historical data on customers have to be crossed with real-time data from transactions. In order to prevent fraud effectively, companies need solutions that combine machine learning techniques with real-time streaming and monitoring.

EURA NOVA has developed the digazu platform to allow companies to go even further in fraud detection. By tapping into machine learning techniques and real-time streaming of data, digazu enables companies to detect fraudulent transactions in real time. With digazu, the deployed data science models make predictions on live events, taking into account a multitude of factors.

By leveraging the multiple data sources and data science models in real time, digazu provides a robust and agile solution to detect anomalies and prevent fraud. The result is that not only can fraud be detected when it happens, but more importantly, fraud can be prevented before it’s too late.

Check out the digazu website or read other digazu stories