What is Agentic AI and How Does Fraud Detection Benefit From It?
Agentic AI systems connect to enterprise data and act autonomously, making independent decisions, and adapting new information to solve complex, multi-step challenges with minimal human intervention. Because AI agents can make context-aware decisions and execute tasks dynamically, they are beneficial for any use case that requires real-time anomaly detection, data analysis, and preventative action or response.
Fraud detection stands as a pivotal challenge for businesses across all industries, from financial services to e-commerce and beyond. The consequences of inadequate fraud detection systems can be devastating for companies; as unauthorized credit card transactions, digital wallet hacks, AML (Anti-Money Laundering) and KYC (Know-Your- Customer/Client) fraud, and other malicious activities exploit vulnerabilities in applications. This leads to significant negative outcomes like lost revenue, regulatory fines, customer churn, and damage to brand integrity. Let’s explore modern tools that help businesses outrun fraud.
Image One: Outrun Fraudsters with Agentic AI
A leading cause of inadequate fraud detection is outdated and complex solutions that struggle to process perpetually increasing transactions. Another is incompatibility with modern technologies like AI-driven fraud detection systems. Many businesses already grapple with time-sensitive SLAs (Service Level Agreements) mandated by regulatory bodies around the world, including the FCA, SEC, SBS, and NFRA.1
Image Two: Outdated, Complex, and Siloed Systems Struggle To Support Modern Fraud Detection
Thankfully, modern architectures and infrastructures provide real-time processing and dynamic scalability to accommodate increasing data volumes and the computational intensity of advanced algorithms. This shift not only enhances fraud detection outcomes but also ensures compliance with industry standards. As fraudsters grow increasingly sophisticated, businesses must continue to adopt advanced technologies capable of outmaneuvering emerging threats.
Next, let’s dive into how agentic AI and stream processing help to accomplish this goal.
Agentic AI and stream processing are two transformative technologies that, when used together, form a powerful solution for combating fraud effectively. Agentic AI introduces autonomous decision-making and continuous learning, enabling systems to adapt dynamically to new patterns of fraudulent behavior. Stream processing ensures the real-time ingestion and analysis of data, providing the real-time insights necessary for timely intervention. Together, these innovations offer a robust framework for addressing the complexities of modern fraud detection.
Image Three: Real-Time Data & AI
Agentic AI is a paradigm shift in how typical AI systems operate. Whereas traditional AI models rely on static training data and struggle to adapt to new or unforeseen anomalies, agentic AI introduces autonomy, adaptability, and contextual awareness. These systems operate independently, analyzing streams of data, identifying patterns, and taking actions without human (or manual) intervention. They continuously learn from new data in real-time, updating their knowledge base to improve accuracy over time. Moreover, agentic AI understands the broader context of transactions, user behavior, and environmental factors, enabling more nuanced fraud detection. The benefits of this approach are profound: proactive fraud prevention, adaptability to new threats, and reduced false positives.
Online banking is a good example where agentic AI can flag unusual login attempts, large transfers to unfamiliar accounts, or atypical spending patterns—all while adapting to legitimate changes in customer behavior.
A critical component of effective fraud detection is ensuring that AI models have access to the freshest data. Fraudulent activities often unfold rapidly, leaving little room for delayed responses based on stale data. The leading stream processing solutions play a vital role in ingesting, analyzing, and acting on event-driven data in sub-200 milliseconds, rather than accessing it in a database at a later time. By analyzing data as soon as it’s generated, stream processing enables real-time insights and actions. Fresh data is essential for fraud detection because it provides immediate visibility into emerging threats, allows AI models to stay ahead of dynamic fraud patterns, and incorporates contextual information such as geolocation, device type, and session duration. Stream processing pipelines ensure that agentic AI systems receive a constant flow of relevant data, empowering them to make timely and accurate decisions.
To further enhance the speed and accuracy of AI model output, Model Context Protocol (MCP) is an emerging standard that enables AI models to connect to external data sources in real-time, without the need for customized integrations. This provides much greater accuracy for real-time queries, reduces hallucinations, and provides a standardized approach for models to access the freshest data without developers having to spend time building and maintaining data integrations.
Image Four: A Modern Solution– Ververica & Agentic AI for Real-Time Fraud Detection
When implementing agentic AI for fraud detection, choosing the right stream processing solution is crucial. Ververica’s Unified Streaming Data Platform is the premier choice, boasting robust capabilities and dynamic scalability. Ververica supports high-throughput, low-latency stream processing, making it perfect for applications that require instant responses. It seamlessly scales to handle several billions of transactions per second, ensuring the highest performance under any load. The platform integrates effortlessly with existing data sources, analytics tools, and machine learning frameworks, simplifying the deployment of agentic AI workflows. Built by the original creators of Apache Flink®, and 100% compatible with open-source Flink, Ververica provides enterprise-grade, mission-critical capabilities that guarantee the highest performance, consistency, availability, and security, to ensure zero downtime and zero data loss. Its unified architecture combines batch and stream processing in a single platform, reducing infrastructure complexity and stream processing development.
For fraud detection, Ververica provides end-to-end pipeline support, customizable workflows, and an AI-friendly environment that makes deploying and managing agentic AI models straightforward.
Let’s consider a real-world example. Imagine an e-commerce company facing rising cases of chargeback fraud. Using Ververica’s Unified Streaming Data Platform, the company sets up a real-time fraud detection system powered by agentic AI. Transaction data flows into the platform from multiple sources, including payment gateways, user profiles, and third-party APIs. Ververica processes this incoming data in real-time, enriching it record-by-record with contextual information such as shipping addresses, purchase history, and behavioral analytics. An agentic AI model evaluates each transaction against known fraud patterns and dynamically adjusts its criteria based on new data. Suspicious transactions are flagged immediately, triggering automated actions such as sending verification codes or blocking payments. Over time, the AI model continues to learn from every interaction, further refining its ability to distinguish between legitimate and fraudulent activity. Utilizing this modern fraud detection system, the e-commerce company lowers the cases of chargeback fraud dramatically, reducing cost and payouts for fraudulent activity, and ends up with a better overall customer experience as well.
By leveraging agentic AI and Ververica’s capabilities, businesses reduce fraud and enhance customer trust and satisfaction. In addition, they benefit from seamlessly integrating modern application architectures into their existing infrastructures at a fraction of the time and cost due to the multi-faceted capabilities of Ververica’s Unified Streaming Data Platform.
There is a perception that agentic AI capabilities are overly complex and difficult to integrate within current architectures. Ververica streamlines the unification of agentic AI with legacy systems. With a flexible architecture and extensive compatibility, it is remarkably easy to adopt, even within entrenched legacy environments. Designed to work seamlessly with a wide range of data sources, sinks, and third-party tools, Ververica ensures zero disruption during migration, empowering organizations to modernize their systems and unlock the full potential of agentic AI, while preserving existing investments. In addition, Ververica offers expert knowledge from subject matter experts.
The demand for cutting-edge technologies that match the speed and sophistication of modern cybercriminals continues. Agentic AI brings unparalleled intelligence and adaptability to fraud detection, while Ververica’s Unified Streaming Data Platform ensures that AI-driven applications have access to the freshest possible data, delivering the performance, scalability, and flexibility required for real-time fraud detection. Ververica’s seamless integration with agentic AI workflows makes it an indispensable solution for businesses looking to stay ahead of fraudsters. Outrun fraudsters with agentic AI powered by real-time stream processing to build robust, future-proofed systems that protect business assets and customers.
More Resources
[Use Cases] Learn how to detect and prevent fraud, and build AI systems that learn, adapt, and act in real-time, and more.
[Case Study] Read how KartShoppe leverages Ververica for real-time feature engineering.
1FCA-Financial Conduct Authority (UK), SEC-Securities & Exchange Commission (USA), SBS-Superintendencia de Banca, Seguros y AFP (PERU), NFRA-National Financial Reporting Authority(China), BaFin-Federal Financial Supervisory Authority (Germany).