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Engineering Blog · Post #70

Bulk Email Discard: Clean Your Queue of Duplicates, Spam, and Auto-Replies in One Click

Out-of-office replies, marketing emails, and duplicate submissions cluttering your work queue? InsightUW identifies and removes them in bulk.


The Problem

A monitored mailbox receives 100 emails per day. 70 are legitimate broker submissions. 15 are auto-replies ("I'm out of the office"). 10 are marketing/spam. 5 are duplicate re-sends of the same submission. If each email creates a submission, the UW's queue has 30% junk. They waste time opening, reviewing, and manually discarding each one. Multiply across a team of 8 underwriters and shared mailboxes — that's hours per week spent on garbage.

The InsightUW Approach

InsightUW classifies every incoming email with 5 automated checks before it enters the queue. Emails scoring below threshold are flagged for review or auto-discarded. A bulk discard function lets Underwriting Assistant teams clean the queue in one operation.

graph TD subgraph Incoming["Incoming Email"] Email["From: sarah@company.com<br/>Subject: Out of Office"] end subgraph Checks["5 Classification Checks"] C1["Auto-Reply Detection<br/>'out of office' → -80"] C2["Spam Keywords<br/>'unsubscribe' → -15 each"] C3["Redundant Patterns<br/>'re: re: re:' → -40"] C4["Duplicate Detection<br/>>85% match in 24h → -70"] C5["Empty Content<br/>< 20 chars → -50"] end subgraph Score["Classification"] S1["Score 70-100: Legitimate<br/>→ Create submission"] S2["Score 40-69: Review<br/>→ Hold for manual check"] S3["Score 0-39: Discard<br/>→ Flag for bulk removal"] end Email --> C1 C1 --> C2 C2 --> C3 C3 --> C4 C4 --> C5 C5 --> S1 C5 --> S2 C5 --> S3

Five Checks

Check What It Catches Score Impact
Auto-Reply "Out of office", "automatic reply", "undeliverable", "mail delivery failed" -80
Spam "unsubscribe", "advertisement", "limited time offer", "free quote" (12 patterns) -15 per match
Redundant Multiple re:/fw: chains, "please disregard", "sent in error" -40
Duplicate >85% subject similarity to a submission in the last 24 hours -70
Empty Body under 20 characters with no subject -50

Three Tiers

Score Category What Happens
70-100 Legitimate Submission created normally
40-69 Review Held for manual review before creating submission
0-39 Discard Flagged for bulk removal

Bulk Discard

Queue scan identifies all flagged items. Underwriting Assistant team selects all (or specific items) and clicks "Bulk Discard." Each item gets:
- Status set to "discarded"
- Audit trail entry with who discarded and why
- Only "new" or "extraction_pending" items can be discarded (guards against discarding active work)

LOB-Specific Example

Monday morning queue for the Commercial Lines mailbox (CML):

100 emails processed overnight. InsightUW classification:

Category Count Examples
Legitimate 68 Broker submissions, renewal requests, quote follow-ups
Review 12 Borderline emails — possible submissions with spam-like subject lines
Discard 20 12 auto-replies, 5 marketing emails, 3 duplicate re-sends

Underwriting Assistant Maria Santos opens the queue scan results, sees 20 flagged items, selects all discards, and clicks "Bulk Discard." 20 junk items removed in 3 seconds. She then manually reviews the 12 "review" items — 8 are legitimate (creates submissions), 4 are junk (discards those too).

Clean queue: 76 legitimate submissions ready for FIFO assignment.

What This Means for Underwriters

  1. Clean queue — no more wading through out-of-office replies and marketing spam
  2. Automated classification — 5 checks run on every email before it enters the queue
  3. Bulk operation — discard 20+ items in one click instead of one-by-one
  4. Audit trail — every discard logged with who, when, and why
  5. Safe guards — can't discard items already in review or quoting stage

What's Next

This completes the Task Management blog series. For more InsightUW capabilities, explore our Submission Intake, Auto-Declination, or Missing Info Follow-Up series.


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