Financial institutions are moving beyond simple AI automation to deploy conversational AI agents that execute complex fraud and anti-money laundering (AML) tasks with minimal human intervention. DataVisor, a leading AI-powered fraud and AML platform, announced Vera, a suite of conversational AI agents designed to let compliance teams issue plain-language instructions that the system executes across the entire fraud and AML lifecycle . This represents a fundamental shift in how banks approach financial crime, moving from reactive rule-based systems to proactive, adaptive intelligence.

What's the Real Problem Banks Are Trying to Solve?

The challenge is stark: fraudsters are weaponizing AI to scale attacks faster than human teams can respond. According to DataVisor's 2026 Fraud and AML Executive Report, only 23% of financial institutions have the right infrastructure to defend against AI-driven fraud, yet 74% of leaders identify AI-driven fraud as a top threat . This gap between awareness and readiness has created what the industry calls the "Readiness Gap," where institutions know the danger exists but lack the tools to match the speed and sophistication of modern attacks.

Traditional fraud detection relies on analysts manually reviewing alerts, tuning rules, and investigating cases. This process is slow, inconsistent, and increasingly inadequate against attackers using machine learning to evade detection. Vera addresses this by automating the analyst's workflow itself, not just the detection logic.

How Do Conversational AI Agents Actually Work in Fraud Prevention?

Instead of clicking through dashboards or writing code, fraud teams now interact with Vera through natural language. A compliance officer might say, "Detect unusual cross-border transfers from new accounts," and the AI agent translates that intent into production-ready detection rules, automatically embedding governance controls. The system then monitors transactions, surfaces alerts, and guides analysts through investigations by highlighting key signals and patterns.

Early customer results demonstrate measurable impact across four critical workflows:

• Detection Strategy Design: Converting emerging fraud patterns into detection strategies increased coverage by 2-3x, allowing teams to respond to new threats in days rather than weeks .
• Optimization: Continuous performance evaluation and threshold refinement reduced false positives by 40% or more, cutting alert fatigue that leads analysts to miss genuine threats .
• Investigation: AI agents reduced investigation time by 20-30x while improving consistency, allowing a single analyst to handle cases that previously required multiple reviewers .
• Regulatory Reporting: Automated generation of Suspicious Activity Report (SAR) narratives from case data reduced preparation time by 90% or more, with full transparency and human review built in .

NASA Federal Credit Union, an early customer, reported that Vera strengthened their readiness and accelerated response to emerging threats. A digital financial platform serving 10 million customers across lending, banking, and financial planning also saw meaningful improvements in both speed and accuracy .

What Makes This Different From Previous AI Fraud Tools?

The key distinction is control and transparency. Earlier AI fraud detection systems operated as black boxes, flagging suspicious activity without explaining why. Vera maintains what the industry calls "enterprise-grade governance," meaning every action is logged, human approval is required for critical decisions, and teams can audit or roll back changes if needed . This addresses a persistent concern in financial services: regulators and compliance officers need to understand and justify every decision the system makes.

Vera

"DataVisor's conversational AI agents show how true agentic technology can accelerate fraud prevention without sacrificing control. Initiated through a chat, conversational AI agents turn analyst intent into production-ready controls with governance automatically embedded," said Ian Watson, Head of Risk Research at Celent.

Ian Watson, Head of Risk Research at Celent

The conversational interface is also significant. Rather than requiring analysts to learn new software or work with data scientists to build custom rules, teams communicate in plain English. This democratizes AI deployment, allowing experienced fraud investigators to shape detection strategies without technical intermediaries.

How Does This Fit Into the Broader Fintech Landscape?

DataVisor's announcement reflects a broader industry shift toward what experts call "agentic AI," where systems don't just predict or classify but autonomously execute workflows with human oversight. This is particularly urgent in financial crime, where the cost of false negatives (missing actual fraud) is regulatory fines and reputational damage, while the cost of false positives (flagging legitimate transactions) is customer frustration and operational overhead .

"With Vera, for the first time financial institutions can outpace AI-driven attackers. By unlocking unparalleled speed and intelligence, we are redefining the playing field and enabling a more proactive defense against AI-driven fraud," stated Yinglian Xie, CEO and Co-Founder of DataVisor.

Yinglian Xie, CEO and Co-Founder of DataVisor

The platform protects tens of billions of transactions annually across banks, credit unions, fintechs, marketplaces, and digital payment organizations, positioning DataVisor as a key player in setting the benchmark for AI in financial crime prevention . As fraud becomes increasingly sophisticated and AI-driven, institutions that can match that sophistication with equally intelligent defenses will have a competitive and regulatory advantage.

Steps Financial Institutions Can Take to Evaluate Agentic AI for Fraud Prevention

• Assess Current Readiness: Evaluate whether your institution has the infrastructure, data quality, and governance frameworks needed to deploy AI agents safely, using industry benchmarks like those in DataVisor's Fraud and AML Executive Report .
• Define Clear Use Cases: Start with high-impact workflows like investigation acceleration or SAR narrative generation, where measurable time savings and accuracy improvements can be demonstrated quickly .
• Prioritize Transparency and Control: Ensure any agentic AI system provides full logging, human approval workflows, and auditability, so compliance and risk teams can justify decisions to regulators .
• Plan for Team Adaptation: Recognize that agentic AI changes how analysts work; invest in training and change management to help teams transition from manual investigation to AI-guided workflows .

The financial crime landscape is shifting from a game of faster rules to a game of adaptive intelligence. Institutions that embrace conversational AI agents and agentic workflows are not just improving efficiency; they are fundamentally changing how they compete against fraud that is itself powered by AI.