Data science Use case
fraud detection digazu 2

Data science at the service of fraud detection

Thanks to data science, fraud prevention technology has now made enormous strides. By using algorithms to combine real-time data analysis with historical data analysis (submitted to anomalies and patterns detection), data scientists are able to detect and prevent potentially fraudulent activity before it occurs. Companies are leveraging machine learning models and graphs to detect, predict and prevent, in real time:

    • fraudulent activities, 
    • malicious users that are using unconventional tactics,
    • specific characteristics and phenomenon related to a specific fraud case

Companies are continuously investing massive amounts of money and efforts to protect themselves against the everlasting evolving fraudulent behaviours. Fraud detection is one of the most important issues of the last decade in many industries, including banking and insurance as well as retail, healthcare, and law enforcement.  It is a high-cost threat to the integrity of any organisation.  

The Digazu platforms (MLOps platform & end-to-end data science platform) are allowing your companies to take their fraud detection to the next level. We help your company tap into machine learning techniques and real-time streaming of data. With Digazu platforms, the deployed data science models make predictions on live events, taking into account a multitude of factors. We help your company leverage the multiple data sources and data science models in real time. Let us  illustrate this with two uses cases: 

    1. Insurance sector:  Digazu helped develop graph-based algorithms to identify fraudulent customers’ claims. Our platforms allowed the company 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.

    2. Banking sector: with Digazu’s platforms we helped build a model based on the business knowledge that fraudulent transactions have different characteristics compared 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.

Fighting fraud requires enriching real-time events with historical data. The 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.

With Digazu’s platforms we provide the companies a robust and agile solution to detect anomalies and prevent fraud before it’s too late.

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