✦ Key Takeaways
- Traditional rule-based systems generate high false positives (90%+), burying investigators in busywork.
- Agentic AI moves beyond "detection" to "investigation," autonomously gathering context across data silos.
- System 2 Thinking: Unlike standard LLMs, our agents "stop and think," planning their search path before answering.
- The result: 40% reduction in investigation time and a 60% drop in regulatory risk exposure.
"Compliance is no longer just about checking boxes. It's about finding the needle in the haystack before the regulators do."
The Crushing Reality of Compliance
Banks are drowning in alerts. For every 100 transactions flagged as "suspicious" by legacy AML (Anti-Money Laundering) systems, fewer than 5 are actual financial crime. The rest are false positives—innocent customers trying to send money to family or pay bills.
This creates two massive problems: Customer Friction (blocking legitimate payments) and Investigator Burnout (teams spending 80% of their day clicking "ignore").
Agentic AI in Financial Crime Prevention
Generative AI is not just for chatbots. When wrapped in an Agentic Architecture, it becomes a indefatigable junior analyst. Instead of just flagging a transaction as "High Risk," an AI Agent:
- Observes the alert.
- Plans an investigation strategy (e.g., "Check beneficial ownership," "Review past SAR filings," "Search adverse media").
- Executes tools (SQL queries, API calls to corporate registries).
- Synthesizes a narrative report for the human officer.
System 1 vs. System 2 Thinking
Standard chatbots use "System 1" thinking: fast, instinctive, and prone to hallucination. WebbyButter's fraud agents use "System 2" methodology. They break complex problems into discrete steps, verifying each assertion against primary data sources before proceeding.
The WebbyButter Difference
We don't replace the investigator. We give them a superpower: the ability to see the entire graph of risk in seconds, not hours.
