AI Simulation Report: Finance AI Agent
This is a real Arato simulation of a finance company’s AI assistant. In financial services, a wrong answer isn’t a UX complaint – it’s money moved incorrectly, a number miscalculated, or a compliance step skipped. See exactly what gets surfaced, and what most teams miss before deployment.
No Setup. No integration. Just real findings on how your AI really works.
Simulation Summary & Risk Score
Key Insight: The Simulation Summary gives you the full picture in one view – pass rate, main risks, response time and length, so you know exactly where your AI is excelling and where it’s falling short, before you dig into any specific conversation.
→ Instant risk assessment: Get a high-level, data-driven snapshot of your AI assistant’s readiness, before it touches a real customer’s account.
→ Critical severity tiers: Every finding is tagged by severity based on what matters most to your business, so the failures that put customers or compliance at risk surface as Critical.
→ Operational metrics: Track performance indicators like Response Time and Response Length alongside every finding.
Detailed Behavioral Analysis
Key Insight: Breaking findings into pillars reveals patterns across customer profiles, showing your team exactly where to focus fixes for the highest impact – for example, the same fee-explanation and balance-calculation errors kept surfacing across multiple different customer profiles.
→ Automated Red Teaming: See how your AI assistant handles both routine requests (like updating contact details) and adversarial ones (like an unverified attempt to redirect account access).
→ Critical pillars breakdown: Evaluate performance across all five dimensions, Accuracy, Compliance, Ground Truth, Clarity, and User Experience, that shape how customers experience a claims process.
→ Visual radar chart: Instantly see where your AI underperforms – in this case, Compliance and Ground Truth, the two dimensions tied directly to regulatory exposure.
Ready to map your AI system’s health?
User Journeys & Sessions Analysis
Key Insight: Every finding comes with the evidence behind it – the full conversation, a screen recording showing you exactly where things went wrong, and the session ID, so your team can go straight to the source instead of taking the score at face value.
→ Visual conversation evidence: Review the exact exchange where the chatbot updated a customer’s registered email to an unverified, suspicious address, no identity check, no escalation.
→ Timeline-mapped findings: See the exact moment the chatbot’s identity-mismatch safeguard triggered, and the moment it got overridden anyway.
→ Root-cause analysis: Trace exactly why the chatbot approved a transfer and limit waiver it shouldn’t have, without digging through backend logs.