Auto Insurance Underwriting Workflow Automation

The Challenge: Manual Data Entry Causes Drop-offs

In the highly competitive digital auto insurance market, speed is everything. When prospective customers are asked to manually type out their 17-character VIN, vehicle specifications, and exact registration details, friction increases and conversion rates plummet. Furthermore, manual entry is highly prone to typos, resulting in inaccurate quotes or delayed underwriting processes. Traditional OCR systems fail when customers upload photos taken in poor lighting, at bad angles, or with flash glare. Learn more about our robust capabilities on our Vehicle Registration OCR product page.

Instant Extraction from Smartphone Photos

StructOCR eliminates manual data entry for insurtech apps. Our advanced LLM-backed visual engine instantly parses complex, fragmented, or poorly lit vehicle registration documents. By seamlessly extracting the VIN, Vehicle Class, Make, Model, and engine details directly from a user's upload, you can pre-fill application forms instantly. Our robust fallback mechanisms dynamically capture region-specific regulatory data into our `localized.generic.unmapped_fields` object. Discover how this fits into broader compliance workflows in our Vehicle Registration Solutions guide.

Core Industry Applications

Integration & API Documentation

Easily integrate our full-field OCR API into your business software. For full technical details, visit our Developer API Documentation.

import requests
import base64

# 📸 Test for free with your own image upload: https://structocr.com/developers/vehicle-registration-ocr

with open("auto-insurance-underwriting-ocr.jpg", "rb") as image_file:
    base64_image = base64.b64encode(image_file.read()).decode('utf-8')

url = "https://api.structocr.com/v1/vehicle-registration"
headers = {
    "x-api-key": "YOUR_API_KEY",
    "Content-Type": "application/json"
}
payload = {
    "img": base64_image
}

response = requests.post(url, headers=headers, json=payload)
result = response.json()

if result.get('success'):
    data = result.get('data', {})
    std = data.get('standardized', {})
    print("✅ Standardized VIN:", std.get('vin'))
    
    # Specific workflow fields are captured in the generic fallback
    loc = data.get('localized', {})
    if 'generic' in loc and loc['generic'].get('unmapped_fields'):
        unmapped = loc['generic']['unmapped_fields']
        print("✅ Vehicle Class:", unmapped.get('vehicle_class'))
        print("✅ Registration Date:", unmapped.get('registration_date'))

{
  "success": true,
  "data": {
    "document": {
      "country_code": "US",
      "document_type": "VEHICLE_REGISTRATION",
      "confidence_score": 0.98
    },
    "standardized": {
      "vin": "SAMPLEVIN12345678",
      "plate": {
        "number": "ABC-123",
        "formatted": "ABC 123"
      },
      "make_normalized": "Toyota",
      "year": 2020
    },
    "localized": {
      "generic": {
        "unmapped_fields": {
          "vehicle_class": "Passenger Vehicle",
          "registration_date": "2022-04-15",
          "engine_capacity": "1998 cc"
        }
      }
    }
  }
}