Loyalty Fraud Prevention - Be proactive, not reactive

Enhanced security of loyalty programs

Ultimately, the key benefit of using an ML-driven fraud prevention technology is reducing the risk of experiencing both internal and external loyalty fraud. Such incidents may result in increased churn of program members, reduced customer engagement, negative PR consequences, and some substantial financial setbacks. To put things into perspective, the Loyalty Fraud Prevention Association reported that total loyalty fraud losses reach over $3 billion annually. Let us help you keep your brand -- and your bottom line -- safe.

Enhanced security of loyalty programs

The Science Behind the Solution

Deep Autoencoders

A deep autoencoder is a type of artificial neural network trained to compress and autonomically reconstruct the input. The worse the autoencoder reconstruction, the more likely it is that an analyzed instance is an anomaly or a potentially fraudulent activity. These networks allow for an unsupervised learning process, where no historical fraud instances are necessary to create a useful fraud detection model. However, if they do exist, provided data may positively impact the overall reliability of the model.

Reinforcement Learning

Reinforcement Learning (RL) is an area of Machine Learning in which technology is used to create semi-autonomous models that attempt to find the most optimal way to achieve a given goal. It was incorporated into our solution as a collection of methods for testing the loyalty business rules and looking for potential loopholes and vulnerabilities.

Clustering Methods

Clustering methods group member accounts with similar histories of interactions. This includes models like DB-Scan, kNN, GMM, etc. Detected groups are then used as input to the ensemble classification model. Clustering boosts the overall reliability of the fraud detection module and reduces the number of false positives as the neural network training process operates on a smaller and more consistent dataset.