Loss-run processing

Structure synthetic loss-run records with correct totals and status.

Step 01

Define and Align

Upload sample data, define your output schema, and provide feedback on the hardest cases.

Output schema
PaidNumber · optional

Amount paid, excluding outstanding reserves.

StatusChoose one

Claim status as recorded in the source.

  • open
  • closed
  • reopened
  • unknown
Claim countNumber

Count of distinct claims after deduplicating updates.

Outstanding reserveNumber · optional

Stated reserve remaining on the claim; not provided when absent.

app.sutro.sh/functions/loss-run-processing
Collecting feedback · 1/3
Structure synthetic loss-run records with correct totals and status.
Example 1 of 3

Paid versus incurred

Model input
Document excerpt
Loss run

Synthetic loss: paid $20,000; outstanding reserve $5,000; incurred $25,000.

Model output
Paid
Status
Claim count
Outstanding reserve

Step 02

Model Sweep

Compare quality and cost on your examples, then choose a model or use Sutro’s default.

Compare models on expert agreement and cost. The interactive chart uses illustrative measurements.

Illustrative sweep · select Value or Best to use that model in your function.

Step 03

Deployment

Call Sutro Functions like any other software. Swap the model as soon as something more performant is released.

Selected modelWaiting for sweep results.

Real-time

import sutro as so

input_data = {
    "text": "Synthetic loss: paid $20,000; outstanding reserve $5,000; incurred $25,000."
}

result = so.run_function(
    "loss-run-processing",
    input_data,
)

Batch

import sutro as so

job_id = so.batch_run_function(
    name="loss-run-processing",
    data="loss_run_processing.csv",
)
results = so.await_job_completion(job_id)

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