Automating Insurance Receipt Claims with OCR and Jev

An insurance receipt claim starts with a form, but assessing it often means several manual steps: reading the receipt, checking the claimant's details, finding the applicable policy and deciding whether the expense is allowed.

An AI workflow could prepare that assessment as soon as the form arrives. A vision model reads the receipt; a decision model such as Jev assesses the expense against company policy; the workflow sends the result to the right queue. The opportunity is to reduce repeated checking and give reviewers more time for exceptions.

This proposed workflow design is illustrated below with a synthetic Jev API run.

#From form submission to policy assessment

The claimant submits their details, claim amount and receipt through a form. The workflow retrieves their eligibility records and the approved policy version that applies to the claim.

Qwen3-VL-8B-Instruct is a vision-language model with OCR capabilities. In this design, it would extract the receipt's provider, date, item descriptions, amounts and currency into fields the workflow can check. Missing or unreadable details trigger review.

flowchart TD
  F[Claim form and receipt] --> O[Qwen3-VL-8B<br/>Read and extract receipt]
  O --> V[Validate fields<br/>and match form details]
  V -- Missing or conflicting data --> H[Human review]
  V -- Checks pass --> J[Jev<br/>Assess policy questions]
  P[Applicable company policy<br/>and eligibility records] --> J
  J --> R[Business rules<br/>and review gates]
  R --> Q[Allowed, not allowed<br/>or human review]

The original receipt stays linked to the assessment so a reviewer can verify what the model read.

#What Jev decides

According to TypeSafe's documentation, Jev evaluates supplied information against typed questions and returns structured decisions. Its Choice questions return a selected answer, probabilities and confidence.

For a receipt claim, we would supply the validated receipt details, form data and relevant policy clauses, then ask focused questions:

The company defines the criteria for each answer. Jev assesses the descriptions against those criteria. Dates, reimbursement limits and duplicate claims are checked separately by ordinary business rules using verified records.

For illustration, a receipt description might fit an outpatient benefit. That assessment only becomes an allowed outcome when eligibility, limits and other required checks also pass. An unclear description goes to review.

flowchart TD
  J[Jev policy answers] --> G{Enough evidence and<br/>confidence to proceed?}
  G -- No --> H[Human review]
  G -- Yes --> B{Policy criteria and<br/>business rules pass?}
  B -- Yes --> A[Allowed<br/>Proceed to approval queue]
  B -- No --> N[Not allowed<br/>Reviewer checks before denial]

#A sample run with the Jev API

This sample run uses Jev Dev, a desktop tool for experimenting with Jev. The input describes a fictional SGD 85 GP consultation for a sore throat, assessed against a sample company policy covering outpatient consultations for illness or injury.

Select the screenshot to view it at full size.

The response from jev-1.13.0 shows:

These answers would support an allowed recommendation once the workflow's other checks pass. Receipt fields were supplied directly; this run demonstrates the Jev decision step. OCR and the complete approval workflow were not run. The confidence values describe this one synthetic input and do not establish accuracy on real claims.

#Make the business case with a focused pilot

Start with one claim category and compare assessments with cases your team has already reviewed. Measure processing time, reviewer effort and incorrect allowed or not-allowed outcomes. Set review thresholds from those results; model confidence alone cannot verify a misread receipt.

Record the policy version, extracted fields, Jev answers, rule results and reviewer action for every claim. Agree where sensitive claim data can be processed and who approves payments or denials before connecting the workflow to live operations.

#Bring us the process you want to automate

AIBackends builds and operates AI workflows connected to your forms, documents and business systems. If your team repeatedly reads receipts and applies company policy, contact us to discuss a scoping call.

Bring your current form, policy, sample receipts and approval steps. We can help define a pilot that shows where OCR, a decision model like Jev and human review can reduce manual work.