In this net safety assistance, Yinglian XIe, CEO of DatavisorExplain how the evolution of fraud tactics require adaptive and AI -centered prevention strategies. With fraudsters using generative AI to launch sophisticated attacks, financial institutions must adopt adaptive AI solutions to stay in advance.
Xie highlights the role of data orchestration in real time, automatic learning and integrated safety platforms in the balancing of fraud prevention with transparent user experience.
How do fraud tactics evolve with the rise of generative AI and automated attack tools?
The fight against financial fraud has always been a arms race between financial institutions and fraudsters, and AI has made it a much tighter race. As payment innovations continue, inherited fraud solutions cannot keep pace. Today, you must be able to fight AI with AI to beat fraudsters to their own game.
With many fraudsters taking advantage of generating AI to launch attacks, fraud solutions can take advantage of generator to stay in the lead in many areas. For example, a generative AI has already been adopted to automate the creation and adjustment of rules to provide better detection and save fraud and money teams on test and error methods.
Organizations must have adaptive answers to be ready for fraud and fraud and new and evolving scam attacks. Sophisticated AI technologies and automatic learning models can analyze large sets of data and signals in real time to identify hidden models and correlations through use models, device information, location information, network characteristics. These technologies are essential to identify new and evolving threats, and they improve permanently over time, which is particularly crucial to fighting deep threats.
Many organizations find it difficult to balance fraud prevention and user experience. How should they navigate in this compromise?
Balancing protection against fraud and user friction is a delicate challenge that many organizations are faced. Carrying out an effective balance requires the implementation of strategies and technologies that improve safety without causing unnecessary drawbacks for users.
Having a unified platform that integrates and orchestra different sources of disparate data in centralized intelligence allows a holistic view and is the best way to ensure the balance between protection against fraud and customer experience.
Many organizations had to reconstruct a multitude of fraud solutions to resolve and protect against all different types of fraud, which creates challenges in the provision of a coherent and transparent experience for customers. This is why real -time data orchestration must be associated with advanced automatic learning and AI technology to offer the best protection against fraud while reducing false positives.
How can organizations improve real-time fraud detection without generating excessive false positives?
The continuous improvement of our understanding of AI and the acquisition of the skills necessary to integrate it effectively in the models is essential to maximize its effectiveness, which includes the increase in precision in real time and the reduction of false positives while improving operational efficiency.
Here are some examples of how to take advantage of different types of AI technologies:
- Supervised automatic learning: Improves the detection accuracy and reduces false positives. It is particularly effective when precise fraud labels are available.
- Automatic learning not supervised: Excellent to identify evolving fraud models even when the labeled data is not available.
- AI Generative: The tools fueled by the generative AI allow fraud teams to automatically create and automatically adjust the rules, write and debug the scripts with the help of AI and automatically generate rules descriptions. The implementation of a tool as this considerably improves the operational efficiency of fraud teams.
What are the main signals or data points that organizations should monitor to detect coordinated fraud rings?
Today, fraudsters have access to the most up -to -date technology that facilitates gaps in fraud strategies. This is why having a centralized intelligence center is the key to an effective fraud strategy. Integrate various data sources and effectively manage the analysis and processing of information throughout the account life cycle, commercial units and channels, it is possible to achieve a complete understanding of customer behavior. The use of this approach makes it much easier to detect and prevent coordinated rings and fraud regimes.
For example, a fraud ring making large -scale coordinated attacks involving control and mass registration of accounts could use different IPS and device IDs, IP addresses traced at VPN or data centers, recurrence of specific beneficiary account numbers, etc. Individually, these subtle signals may not immediately raise concerns, but the true force of data orchestration on the capacity of fraud.
The more data to be accomplished, the more clear the models become, allowing the detection and liaison of several accounts involved in the ring. The data orchestration is essential to be able to aggregate large sets of information, which allows organizations to use them in real time to discover and dismantle sophisticated fraud networks.
How should security, risk and fraud teams collaborate to improve fraud prevention efforts?
The most effective security, risk and fraud teams can collaborate to combat new and sophisticated fraud, is to aggregate all the data and signals of all the different systems. This gives an organization a much more complete and holistic vision compared to traditional approaches that tend to be partitioned and reactive, and therefore more vulnerable to fraud attacks. At the same time, organizations must implement a flexible decision flow to allow dynamic action based on a wide range of signals.
