Fraud Detection &
Prevention
Challenges our Clients Face:
Cybercriminals continuously adapt their methods, rendering the existing rule-based system insufficient for detecting new types of fraud. It can only identify fraud based on pre-defined rules, missing out on emerging tactics and sophisticated attack vectors that are yet to be encountered.
What PegasusAI Can Do?
1. Anomaly Detection with Autoencoders:
Deployment of an Autoencoder-based anomaly detection system that learns from normal transaction patterns. By training the Autoencoders on vast amounts of legitimate transaction data, the model will learn what a "normal" transaction looks like. Any deviation from these patterns—indicative of potential fraud—will be flagged for further review.
This approach allows the system to detect previously unknown fraud tactics, as it focuses on recognizing patterns outside of the learned normal behavior, rather than relying on predefined fraud rules.
2. Fraud Simulation with Generative Adversarial Networks (GANs)​
Integration of a GAN architecture to simulate various types of fraud scenarios, to further enhance the system’s robustness. By generating synthetic fraudulent data, the GAN model will be able to "train" the fraud detection system on a wider range of potential fraud patterns, improving its ability to identify even the most complex, previously unseen fraud tactics.
This dynamic learning capability ensures that the model can adapt to emerging fraud trends and stay one step ahead of cybercriminals.
3. Real-Time Detection and Response
Implementation of fraud detection models into the company’s transaction processing system, enabling real-time analysis of transactions as they occur. Suspicious activities will be flagged in milliseconds, and high-risk transactions will be automatically blocked, while lower-risk transactions will be queued for manual review.
This real-time capability minimizes the delay in identifying fraud, significantly reducing the financial and reputational damage caused by fraudulent activity.
4. Reducing False Positives
Through fine-tuning the model’s precision, the number of false positives will be significantly reduced. Instead of blocking legitimate transactions, the AI-driven system uses more nuanced, multi-dimensional data points to make decisions, reducing customer inconvenience while still maintaining robust fraud protection.
This approach also improves internal efficiencies, as the manual review team will have fewer transactions to analyze and can focus on high-risk cases.
Ready to explore the potential of AI for your business?
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(647) 215-4164