Rules have played a significant role in the history of fraud prevention, and while they’re not obsolete by any means, the way we use them has to change. Modern fraud moves too fast, today’s fraudsters are too agile, and rules alone simply cannot keep pace with the rate at which these sophisticated digital criminals adapt and alter their techniques, tactics, and targets. The reactive process of writing a new rule in response to observed fraud is outmoded—by the time the new rule is ready, fraud has moved on. Rules still have something critical to offer, however. Fraud teams can directly write rules and set thresholds for fraudulent account activities, transactions, digital fingerprints, and more, and rules are effective for mitigating losses from known fraud attacks. But that’s not enough. Introducing DataVisor’s Advanced Rules Engine This is why DataVisor is releasing our Advanced Rules Engine (ARE). We are combining the best of rules with the power of AI and machine learning, to give fraud teams a powerful new weapon in the fight against fast-evolving modern fraud. From leveraging automated rule recommendations, to creating advanced rules with AI-enriched features, to managing rulesets and testing rule performance, DataVisor’s Advanced Rules Engine empowers you to rapidly create and deploy intelligent rules that enable early detection and significantly improve detection results while simultaneously increasing efficiency and reducing overhead. Conventional Rules Engines: Challenges The challenges associated with conventional rules engines are familiar ones for anyone engaged in the fight against today’s highly sophisticated fraud attacks. Inability to Detect Fast-Evolving New Fraud AttacksThe most important of these is the inability to detect new and emerging fraud. Given that fraudsters have equal access to transformational technologies like AI and machine learning, it’s no surprise how complex modern fraud attacks have become. Not only are bad actors able to develop innovative new attack types, but they also continue to repackage and bundle different existing attack techniques in new ways. In either case, the result is the same—rarely do any two attacks look the same. Lack of ScalabilityScale is another critical concern. Bot-powered attacks assault businesses and platforms at massive scale, and only the most comprehensive systems can withstand them—and these systems need a great deal of intelligent automation and bulk decisioning options to do so. Advanced unsupervised machine learning capabilities are critical as well, in order to expose the correlations between connected malicious accounts and actions. Operational DrainThere is an operations component to consider as well. Rules need to be continuously updated but exploring and testing rules can take months. Managing rules can be tedious work, particularly when organizations have thousands of rules. New rules need to be written, legacy rules need to be retired, and performance testing needs are ongoing. Taken together, rule management is an operational drain on time and resources. The Advanced Rules Engine Advantage DataVisor is introducing the Advanced Rules Engine to bring a new level of innovation to rules, by fundamentally solving the challenges posed by traditional rules engines, and by delivering exceptional benefits to organizations across industries and use cases. DataVisor’s Advanced Rules Engine empowers organizations to create, manage, and systematically organize rules and rulesets, test and optimize rule performance with advanced capabilities such as backtesting and forward testing, and get automated rule recommendation to immediately use new rules to detect new fraud attacks. Additionally, through a seamless integration with our Feature Platform, teams can leverage comprehensive AI-enriched features to create advanced rules, and combine rules engine results with machine learning engine results in a centralized decision-making process that promotes enhanced performance. Automated Rule Recommendation to Detect New Fraud PatternsThe ARE automatically recommends new rules to capture unknown fraud patterns, leveraging advanced AI technologies. Using automated rule recommendations, fraud and risk teams will no longer spend days and weeks exploring new rules as new attacks continue to bypass their existing detection systems. They will instead have immediate access to hundreds of new rules every day, and be able to use built-in testing capabilities to select and create the right rules for optimal performances. The entire rule creation process can be shortened from weeks to hours, freeing fraud and risk teams to focus on more critical matters. Rule Performance Monitoring and TestingFraud teams are now empowered to continuously enhance rule performance with the ARE. Teams can track rule performance over time—along with detected accounts—with visualized insights. DataVisor’s Advanced Rules Engine provides both backward testing and forward testing to ensure optimal rule performance. Fraud team can run backtesting on historical data to validate detection performance, with full flexibility to choose the data’s timestamp and sample set. Meanwhile, performance testing enables users to publish rules in a test mode before deployment. The ARE will then monitor rule performance on an ongoing basis so that fraud teams can make data-driven decisions about whether to archive or deploy the rules. Advanced Rules Engine: Benefits Scalable Rule and Ruleset ManagementThe ARE efficiently manages complex rules at scale with maximum flexibility. Fraud teams can create rulesets to organize different rules, while testing and taking action on multiple rules in bulk. Users have full control to append messages as metadata to the rules, apply logic combinations within rules, add tags for various use cases, and track the status of each rule. The ARE provides full support for managing thousands of rules across a large team. Sophisticated Rule Creation with the Feature PlatformThe ARE seamlessly integrates with DataVisor’s Feature Platform, enabling fraud teams to create rules directly from AI-enriched and deep learning features; this includes features from IP addresses, emails, user names, time stamps, devices, transactions, and more. Powered by superior domain expertise, features are uniquely tailored for every given use case, and will provide the most relevant and best-performing features for each scenario. Actionable Rules with Customized DecisioningUsing the ARE, organizations can benefit from flexible implementations of rules. With the Rule Trigger Counter function, fraud teams can take different actions based on different policy violations. Consider the example of an ATO scenario—if an account is compromised for the first time, the rule will trigger an action to suspend the account; if the account is compromised for two or more times, a customized email will be automatically sent to the customer. Actionable rules enable customized decisioning and efficient case management. Conclusion DataVisor’s Advanced Rules Engine brings a new level of innovation to rules by infusing the ease and simplicity of conventional rules-based approaches with the future-facing power of AI and machine learning. In doing so, DataVisor empowers fraud and risk teams to save time and improve efficiency even as they increase detection accuracy and capture more fraud sooner. Only a truly comprehensive fraud management solution—built on deep domain expertise, vast global intelligence, powerful machine learning algorithms, and scalable big data architecture— is up to the task of staying ahead of fast-evolving modern fraud. The release of DataVisor’s Advanced Rules Engine introduces a powerful new component to our comprehensive suite of fraud management solutions. View posts by tags: advanced rules engine | Feature Engineering | rules engine | rules-based systems Related Content: How To Accelerate Feature Engineering From Weeks to Minutes Is Protecting Your Customers Against Fraud Your Most Important Competitive Advantage? React, or Prevent? Why Organizations Must Embrace A Proactive Approach To Fraud Management 订阅后方便随时了解最新的欺诈行业见解和智能风控信息。 Thank you for subscribing.