Insurance

Automate workflows, improve
accuracy, and reduce
processing time.

In the insurance industry, a wide range of documents are processed using Intelligent Document Processing (IDP), Robotic Process Automation (RPA), Machine Learning (ML), and Natural Language Processing (NLP) to automate workflows, improve accuracy, and reduce processing time. Here’s a breakdown of the types of documents commonly handled with these technologies:

Claims Processing Documents

First Notice of Loss (FNOL)
Medical reports and bills
Repair estimates
Police reports
Claim forms
Photos and attachments

How they’re processed:

  • IDP extracts and classifies fields like claim number, date of incident, etc.
  • RPA routes the documents to the correct systems/workflows.

Underwriting Documents

Applications and proposal forms
Medical records (for life/health insurance)
Inspection reports
Credit reports
Risk assessment questionnaires

Processing use:

IDP reads and extracts structured data.
ML scores risk based on historical data.
NLP interprets free-text responses in questionnaires.

Policy Servicing Documents

Policyholder communications (emails, letters)
Change requests (address, beneficiaries, coverage, etc.)
Renewal documents
Endorsements and riders

Processing use:
  • NLP classifies the intent of emails/letters.
  • RPA performs routine updates in policy systems.
  • IDP automates data capture from forms.

Regulatory & Compliance Documents

KYC documents (IDs, proof of address)
Audit and compliance reports
Regulatory filings
Sanctions screening reports

Processing use:
  • IDP & ML verify identity documents.
  • RPA automates compliance checks and cross-referencing.
  • NLP checks for red flags in free-text entries.

Billing and Payment Documents

Invoices
Receipts
Premium payment forms
Bank statements (for fraud detection or financial validation)

Processing use:
  • IDP extracts payment details.
  • RPA reconciles payments and triggers workflows.
  • ML detects anomalies or potential fraud.

Customer Interaction Documents

Chat transcripts
Email communications
Call center notes

Processing use:
  • NLP performs sentiment analysis and intent detection.
  • ML can suggest actions or route to agents.
  • IDP extracts relevant case numbers or customer details.