"Big risks require big data thinking"Every year, fraud can cost your company several hundred thousand euros. And that’s not to mention the negative impact it can have on your brand image.
Because dealing with fraud is an essential part of being competitive, you’ve decided to handle it internally.Every day, your teams of data scientists work on analyzing suspect behavior, detecting weak signals and creating models to produce effective algorithms.Their aim is to:
The fact remains that the use of data to detect fraud is still very limited. While 72% of companies believe that big data can play a key role, only 2% report exploiting big data technologies, and 11% use statistical analysis or data mining ... *
- Detect suspect behavior as early as possible
- React as quickly as possible
- Improve your operational performance
From designing to deploying detection algorithms
- Designing an algorithm is only part of the solution. You then need to implement and deploy it. And to undertake this, you first need to build an exhaustive technological environment.
- The real challenge is to successfully manage the multitude and interrelationship of different functional layers.
- How can you ensure you maintain the intellectual property of your machine-learning algorithm – and the fruits of its learning?
Towards Data Science operationalizationWith ForePaaS, you can optimize your algorithms for detecting fraud
- Thanks to full platform automation, ForePaaS allows you to leverage your data science processes in record time
- Maintain ownership of your intellectual property: by internalizing your algorithms, you’ll ensure your competitors are denied access to your sector-specific solution