How We Built an AI Routing Engine That Works for Insurance (Not Just Marketing Forms)
Most “AI routing” tools are designed for marketing – tagging leads, assigning CRM stages, or routing emails to sales reps. That’s not how insurance works.
We needed a routing engine that could:
- Read a Workers’ Comp app
- Flag it as a quote-ready submission
- Route it to RQB with correct metadata
- Log the task, timestamp the quote, and trigger downstream tasks
So we built one. Here’s how it works – and why it matters.
1. Insurance-Specific Intent Recognition
Generic AI tools flag keywords like “urgent” or “follow-up.”
We train our GPT model to detect:
- LOB-specific document types (e.g., audit packet vs. FNOL)
- Carrier instruction language (“attach loss runs”)
- Submission status (“final quote attached” vs. “draft only”)
Result: 85-90% accuracy across top 10 intake categories.
2. Document-to-Workflow Mapping
Once classified, each document is mapped to:
- A workflow (quote prep, audit review, FNOL intake)
- A task type (create COI, enter endorsement)
- A system destination (AMS360, Expert Insured, RQB)
Example: A loss run PDF triggers a bind-review task inside the underwriter’s queue with flagged confidence score.
3. Confidence Threshold Logic
Routing doesn’t mean guessing. We:
- Set minimum confidence thresholds per workflow
- Log fallback for human review when scores dip
- Allow override or retraining based on pattern frequency
4. Speed-to-Task = Seconds, Not Hours
With traditional intake, an underwriter sees the email after:
- 1-2 hours of backlog
- Maybe a folder sort or a VA download
With Selectsys routing:
- Email hits inbox
- AI reads body, tags type, extracts metadata
- Task shows in system – within 15–45 seconds
Test Case
Client: MGA with 4K monthly inbound emails + docs
Problem: Human triage delayed quoting by 1–2 business days
Solution: AI listener + GPT classification + routing rules + BPO handoff
Result: Quoting triggers now fire same-day. FNOLs log instantly. Quote volume up 25% without more staff.
FAQs
What’s the difference between this and a chatbot?
This isn’t a chatbot. It’s backend triage logic built for workflows – not for user interaction.
Can I configure what gets routed where?
Yes. We use YAML-style rule sets and custom tagging. You define triggers, thresholds, and destinations.
What happens if the AI routes incorrectly?
The fallback kicks in – human QA reviews the task. Errors are logged and model retrained.
Does this replace my CSR or just help them?
It helps them. It clears the inbox so they can focus on approvals, questions, or QA.
Smart Intake Is the Future of MGA Ops. But It Needs Insurance-Specific AI.
Try the Selectsys routing engine on your most painful inbox – and see what real triage looks like.
