What’s inside?
In this playbook, you will:
- Understand how AI agents can close the fraud and AML build gap, moving from months-long builds to days
- Learn how to turn requirements, SOPs, and regulatory guidance into complete, connected fraud and AML programs
- Discover practical AI use cases, governance controls, and KPIs for building and updating programs faster
- Evaluate AI agents, follow a 90-day implementation roadmap, and assess your readiness with the AI-Built Program Readiness Checklist
Chapter 1
The Build Gap
Why programs take months while financial crime changes every week
Fraud and AML leaders rarely struggle to know what their program should do. The detection gap is usually clear after an investigation. The new regulatory indicator is written down in a bulletin. The investigation procedure already exists as an SOP. The challenge is the distance between knowing what the program should do and having a program that does it.
That distance is the build gap. For a new real-time fraud use case, the industry norm is three to six months from decision to deployment. Updates are faster, but they still move through the same queue of configuration work, testing, and engineering time.
Year-After-Year,
the Industry’s Choice
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About DataVisor
DataVisor is the AI-native real-time decisioning engine for fraud and financial crime prevention.
As AI transforms both fraud attacks and fraud defense, DataVisor helps financial institutions, payment providers, and digital businesses detect, investigate, and stop sophisticated and previously unseen threats in milliseconds across billions of transactions. Combining adaptive machine intelligence, consortium intelligence, and emerging agentic AI capabilities, DataVisor enables organizations to modernize fraud operations, improve customer experience, and stay ahead of rapidly evolving financial crime. DataVisor is trusted by leading financial institutions, payment innovators, Fortune 500 enterprises, and digital businesses worldwide.