Straight-Through Processing: From 10% to 90%

Straight-through processing is the number every insurance executive quotes and almost nobody achieves. The industry benchmark has moved from 10–15% to 70–90% at the carriers who've cracked it. That's not a marginal efficiency gain — it's a different cost structure.

So why is your STP rate still stuck in the teens?

Because STP gets treated as a workflow project. It isn't. It's a data problem wearing a workflow costume.

What STP actually requires

Straight-through means a quote, policy, or claim goes from submission to completion with zero human touches. For that to happen, every decision point in the chain has to be answerable automatically. Every single one. A chain of nine automated steps and one manual check isn't 90% straight-through — it's 0%. One human touch and the whole thing is a manual process with extra steps.

That's the bar. And it means the constraint isn't the workflow engine — it's whether the data needed at each decision point is present, trusted, and available right now.

The three reasons things fall out

Track your fallout reasons and they'll cluster into three buckets. All three are data.

1. Missing data. The field the rule needs is blank. Roof age is unknown, so the rule can't fire, so it routes to an underwriter — who looks it up manually from the same sources you could have integrated. Every blank field is a manual queue.

2. Untrusted data. The field exists, but nobody believes it. So a human "verifies" it. This is the most insidious kind: you've automated the decision and then added a person to check the automation, which is worse than not automating at all. Trust is a data-quality score, not a vibe.

3. Late data. The signal exists and is trustworthy, but it arrives tomorrow. Your fraud check runs on an overnight batch, so the claim waits, so the process isn't straight-through. Latency turns a good decision into a manual queue.

The uncomfortable arithmetic

Here's why incremental effort disappoints. If each of ten decision points is automated 90% of the time, your end-to-end STP isn't 90%. It's 0.9^10 — about 35%.

That maths explains everything about why STP programmes underdeliver. Teams celebrate improving one step from 80% to 95% and the end-to-end number barely moves, because the weakest link still routes everything to a human. STP is a chain, and chains are governed by their worst point.

The implication: find your worst decision point and fix that. Not the one that's easiest to improve. Not the one the vendor demo covers. The one that dumps the most volume into the manual queue.

How to actually get from 15% to 90%

Instrument fallout first. Before changing anything, log why each case went manual — field-level, not category-level. "Referred to underwriter" is useless. "roof_age missing" is a work order. Most carriers can't do this today, which is why their STP programmes are guesswork.

Rank by volume, fix top-down. You'll almost always find that three or four fields cause most of your fallout. That's your roadmap, and it's usually much shorter than expected.

Close data gaps with enrichment, not people. If roof age is missing, integrate a property-data provider. The cost of the data feed is trivially less than the underwriter hours it eliminates.

Fix latency where the decision needs it. Move fraud and eligibility signals into a real-time path. Leave everything else on batch — streaming what doesn't need streaming is expensive theatre.

Score your data and let rules trust the score. Auto-proceed above a confidence threshold, escalate below it. Now "untrusted data" becomes a tunable dial instead of a blanket human review.

Accept that some cases should be manual. The goal isn't 100%. Complex, high-value, and genuinely ambiguous cases should reach an expert. STP is about making sure the simple 80% never does.

The point

Nobody gets to 90% STP by buying a better workflow tool. They get there by making the data at every decision point present, trusted, and timely — and by being ruthless about finding which single point is dumping the most work on humans.

The workflow was never the bottleneck. It's just where the bottleneck becomes visible.

We build the real-time, quality-scored data foundations that make straight-through processing possible. More at IntelliBooks.

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