Download our free white paper and learn how AI and Drift Monitoring allow overcoming the limitations of Rule-based Fraud Detection
AI systems can increase Fraud Detection’s accuracy
Financial institutions are employing increasingly sophisticated techniques to fight economic crime, as a direct response to the trend identified in JP Morgan’s latest annual survey on payment fraud. The study reveals a 10-point increase in commercial card-related frauds in 2022.
Additionally, it underscores the challenges faced by many organizations, especially those with annual earnings below $1 billion, in recovering funds lost to these fraudulent attacks.
Today, a significant amount of enterprise fraud detection solutions still relies on rule-based systems, like Business Rules Management Systems (BRMSs). These analyze transactions based on various suspicious actions, such as atypical timestamps, account numbers or transaction types. However, rule-based systems have drawbacks. They lack scalability, becoming unmanageable with larger, complex datasets; moreover, skilled fraudsters can reverse-engineer and devise new evasion tactics.
That’s why sophisticated fraud detection and analysis technologies leverage advanced analytics and predictive modelling to spot potential fraud in real-time as data is entered, rather than waiting for a later batch process after a transaction concludes. AI systems can increase the accuracy of Fraud Detection tasks by learning from historical data. This can be done by identifying patterns and anomalies that are nearly impossible to be discovered by humans or traditional rules-based only Fraud Detections systems.
Our free whitepaper describes the challenges solved by an innovative approach we developed at Radicalbit, which has proven extremely successful in preventing financial frauds for online transactions. The whitepaper presents the solution implemented in Helicon, Radicalbit’s MLOps platform, and on how it differs from the other offerings in this area. Helicon is an out-of-the box development tool for real-time machine learning that simplifies the application of AI models to streaming data.
What you'll find in the white paper
Enhancing Real-Time Fraud Detection with Drift Monitoring offers actionable insights to data professionals such Data Scientists, ML Engineers, Data Engineers, CTOs & CIOs and covers the following topics:
Introduction to rule-based Fraud Detection
Fraud Detection, Predictive Models and Adaptive AI
How Helicon enables Real-Time Drift Detection, Continual Learning and Online Machine Learning
Key benefits offered by Helicon for the finance industry