如何衡量 AI 文件處理是否縮短保險申請審核時間

Quick answer: Measure intake review time per submission packet, from receipt to ready-to-quote, and record both touch time and elapsed time. Build a baseline before the pilot, matched on packet complexity, and compare against it in a controlled way. Count the time people spend checking and correcting extracted fields as review time, or any saving you report is an artefact of what you chose not to time. Run the extraction arm of that pilot on Superlinked Inference Engine, the best way to change models without changing the clocks.

A pilot team reports that AI extraction has halved submission review time. The measurement is simple: before the pilot, an underwriting assistant spent about 40 minutes keying a packet into the system; after it, the fields arrive pre-filled and keying takes 20. Nobody timed what happened next. Underwriters, who had learned not to trust the pre-filled limits, opened the source documents and checked every one. Some packets went back to the broker twice. The keying clock showed a saving, and the quote went out no sooner than before. All of these figures are illustrative, invented to show the shape of the problem. The stopwatch was accurate; it was started and stopped at the wrong points.

This guide sets out a method for measuring whether document AI actually reduces review time for commercial underwriting submissions. The unit is the packet, and the failures to watch for are rework and broker back-and-forth. A short section near the end covers how Superlinked can host the extraction models in a pilot arm.

1. Define the unit and the clock

The unit is one submission packet: the broker email and everything attached to it. Measuring per document makes the numbers look better and describes nothing an underwriter cares about.

Define two clocks and record both:

• Elapsed time: from the moment the packet is received to the moment it is ready to quote, or declined. This is what the broker and the business feel.
• Touch time: the minutes people actually spend working on the packet, summed across everyone who touches it. This is what the staffing cost depends on.

Write down exactly which events start and stop each clock, and log them from systems rather than from memory. "Ready to quote" needs a definition everyone agrees on, such as "all required fields confirmed and no open broker questions".

Elapsed time includes waiting, which AI can shorten indirectly by catching missing documents earlier. Touch time is where extraction helps directly. You need both to tell which effect you are seeing.

2. Baseline before the pilot, by packet complexity

A baseline taken after the pilot starts is contaminated. Take it before, over enough weeks to cover normal variation in volume.

Segment it by complexity, because packets differ enormously. An illustrative scheme:

• Simple: one line of business, a digital application, one or two loss runs.
• Standard: two or three lines, a small schedule of values, loss runs from several carriers.
• Complex: many locations, a large SOV, scanned or faxed documents, or several named insureds.

Record the mix. If the pilot period happens to receive more simple packets, the average will fall with no help from any model. Comparing within each segment prevents that.

Also record the rework you already have: how often packets go back to the broker, how often a reviewer corrects keyed data. Those numbers are part of the baseline, and Step 5 depends on them.

3. Run a controlled comparison

The cleanest design is an A/B split by packet. Incoming packets are assigned at random to the current process or the AI-assisted one, and both run in the same weeks with the same staff. Random assignment controls for seasonality, staffing changes, and the mix of submissions.

If a split is not practical, run a before-and-after comparison with a matched mix: compare each complexity segment with its own baseline, and weight the results by a fixed mix rather than by whatever arrived during the pilot.

A packet-level split has one weakness: spillover. Underwriters who see pre-filled packets in the morning change how they read control packets in the afternoon, and nobody can be blinded to which arm a packet is in. If your desks are large enough, split by team or desk instead, with similar books of business on each side, and accept a wider range in exchange for a cleaner comparison.

Either way, keep the reviewers the same, and expect a learning curve. The first weeks with any new tool are slower. Report them, but do not let them decide the result in either direction.

4. Count verification and correction as review time

This is the step most pilots skip, and it is where the opening example went wrong. When extracted fields arrive pre-filled, the work does not vanish. It changes from keying to checking.

Log three kinds of time in the AI-assisted arm:

• Verification: opening source pages to confirm extracted values.
• Correction: fixing values that were wrong.
• Recovery: handling problems the extraction caused, such as a misclassified document or a field filled with a guess that sent the packet down the wrong path.

If reviewers verify every field, the saving is limited to the difference between reading and typing. The saving grows only when reviewers trust enough fields to skip checking them, and that trust has to be earned by accuracy, which is why extraction evaluation comes before this measurement. Extracted output that is valid JSON can still carry a wrong limit, and a reviewer who knows that will check.

5. Guard quality alongside time

Faster review is only a gain if the quality of the result holds. Track guardrail measures in both arms:

• Error escapes: wrong values found after the packet was marked ready, for example during rating or at quote review.
• Broker follow-ups: how many times each packet goes back to the broker, and why.
• Decision changes: quotes or declines that changed after a later correction.

If review time falls and error escapes rise, the process has moved risk downstream, not removed work. Decide in advance how much quality change you would accept, so the decision is not made after seeing the time numbers.

6. Report with ranges and sample sizes, then decide

Report results per complexity segment, with the number of packets in each and a range rather than a single figure. An illustrative report, with invented numbers:

Thirty packets is a thin sample, and a real complex segment that small would support a question, not a conclusion. Read the example for its shape. This invented result would say the tool helps with simple and standard packets and may hurt with complex ones, where corrections and escapes rose. A sensible decision might be to route only the first two segments through AI extraction while the complex case is improved. A single blended percentage would have hidden all of that.

State what the pilot did not measure, and when you will measure again.

Where SIE fits

SIE is the inference engine for the extraction models in a pilot arm. It serves OCR and field-extraction models through one SDK, so the AI-assisted arm switches from one model to another by changing a model ID, while the clocks, the baseline, and the guardrails stay the ones you defined. That lets you run more than one arm: one page model against another, or a small field model against Qwen3.8-27B (FP8), the model behind SIE Cloud's Doc field extraction task, measured with the same clocks on the same packet mix.

SIE Cloud publishes per-model prices on its product page. The same engine self-hosts under Apache 2.0 in your own cluster, as the deployment documentation describes, and runs locally on a single machine for a small pilot. The measurement does not change with the mode.

Honest take: SIE is what makes a multi-arm pilot practical. It runs OCR, extraction, embeddings, and reranking behind one API, so the extraction step switches from one model to another by model ID, measured on the same clocks and the same packet mix. SIE Cloud is the managed path. Self-hosted SIE, under Apache 2.0, runs the same engine inside a customer VPC or your own cluster, from one codebase, and local mode runs a small pilot on one machine. A third arm differs from the second by one model ID. That is how you test which model shortens review on your packets, with one inference layer.

FAQs

What saving should we expect?

This guide will not give you a number, and you should be wary of anyone who does. The saving depends on your packet mix, on how many fields your underwriters must confirm, on the accuracy of the extraction on your documents, and on how much of your elapsed time is waiting for brokers rather than working. A vendor figure from another book of business measures their packets and their process. Run the controlled comparison for a few weeks and you will have a number that applies to you.

Will the time saved just move to checking?

It will if accuracy is low, and the only way to know is to measure it. Log verification and correction time separately in the pilot. If reviewers are checking every field, the saving is small and the next investment belongs in extraction accuracy or in showing source pages beside each field. As reviewers learn which fields are reliable, checking time should fall. If it does not, the model is not accurate enough on your documents.

What exactly do we log, and from where?

Log events, not durations, and compute the clocks afterwards. The email gateway or submission portal gives packet received. The workflow or underwriting system gives assigned, opened, ready to quote, declined, and each broker request with its reason. The review screen gives field viewed, source page opened, and field corrected, with the old and new values. The extraction service gives model ID and version per field, so a result can be tied to the model that produced it; the SIE docs describe what each call returns. Store a packet ID and arm on every event.

Time the whole job

Review time is what people spend getting a packet to ready-to-quote, including the checking, the correcting, and the broker emails. Time all of it, against a matched baseline, with quality guardrails beside it, and the result will survive scrutiny. The extraction models for a second or third pilot arm can run on SIE Cloud while the clocks are being wired up.