Report: Fraud Teams Grow Despite AI Adoption

- The 2026 SEON report surveyed 1,010 fraud, risk, and compliance leaders at the director level or above across various sectors, including payments, fintech, and financial services. The research revealed that while 98% of these organizations already use AI in their daily workflows, most still anticipate increasing both their budgets and staffing for fraud prevention in 2026 due to rising fraud complexity. - A primary challenge driving the continued need for human teams is data fragmentation; 80% of leaders find it difficult to get a unified view of their data, and less than half (47%) have fully integrated workflows between their fraud and AML systems. This complexity necessitates human oversight to connect insights across siloed systems. - Agentic AI, or multi-agent systems, are a key architecture being adopted in insurance to combat fraud. These systems use coordinated, specialized AI agents for tasks like document analysis, fraud detection, and customer communication, which improves accuracy by over 30% compared to monolithic AI systems and reduces claims processing time from days to seconds. This approach mirrors the functional structure of a human claims department, allowing for scalable and parallel processing of different claim types. - For Principal-level individual contributors, technical leadership involves guiding high-level architecture and influencing multiple teams without direct authority. This requires shifting focus from personal output to multiplying the impact of the entire team by setting technical standards, mentoring other engineers, and ensuring engineering decisions align with broader business strategy. - In insurtech, AI is reshaping core workflows like underwriting and claims processing by automating data collection, risk stratification, and even generating policy recommendations. Large Language Models (LLMs) are now used for intelligent document processing—handling OCR, entity extraction, and summarization—which can reduce manual processing times from days to minutes. - Backend architecture for modern insurance platforms must be scalable to handle the massive data volumes required for AI-driven underwriting and real-time fraud detection. The move to cloud-native, API-enabled ecosystems is critical for integrating various data sources, from IoT devices to customer documents, and creating a unified data view. - The rise of generative AI is a double-edged sword; while it enhances fraud detection, it also fuels more sophisticated and scalable attacks like deepfakes and synthetic identity fraud. This has led to a surge in multi-step, coordinated fraud attacks, which grew by 180% year-over-year in 2026, forcing a shift toward real-time behavioral analysis rather than relying solely on static authentication checks. - For technical founders in fintech, a key trend is the development of platforms that offer a unified "command center" for both fraud and AML operations. Venture trends favor solutions that provide code-free, customizable rules and integrated case management, allowing compliance teams to adapt quickly to new threats and jurisdictional regulations without vendor dependency.