LOYALTI FRAUD PREVENTION
Be proactive, not reactive
As loyalty programs grow in popularity and scale, so do scammers. Loyalty Fraud Detection targets suspicious behavior and detects anomalies to prevent an array of fraud techniques. Whether you allow point payments or process large volumes of data, Loyalty fraud Prevention is designed to keep both you and your customers safe.
Proactive fraud prevention
Loyalty fraud detection module guarantees that any newly detected behavior vastly different from the trained model will be detected and stopped before any damage is done. If there are ways to commit fraud that have not been detected in the past, our module will find and prevent them in real-time. Stay a step ahead of the fraudsters with this data-driven proactive security measure.
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.
Reduced manual review workload
Our AI-powered loyalty fraud prevention module helps reduce the rate of false-positive cases that require manual reviews conducted by contact center agents or loyalty fraud analysts.
The Science Behind the Solution
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. If they do exist, however, the provided data may positively impact the overall reliability of the model.
Reinforcement Learning (RL) is an area of Machine Learning in which technology is used to allow for the creation of semi-autonomous models attempting 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 group member accounts with similar histories of interactions. This includes models like DB-Scan, kNN, GMM, etc. Detected groups are then used as an 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.
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