AI-Driven Insurance Transformation: Streamlining Claims & Support

Discover how ez-ai.nz transformed operations for a New Zealand insurance broker by automating claims processing and customer support, drastically reducing errors and response times.

Background

Our client, a medium-sized insurance broker in New Zealand, was grappling with inefficiencies in their claims processing and customer support operations. Manual data entry, lengthy verification processes, and slow response times were resulting in errors, delayed claims settlements, and unsatisfied customers.

The project objectives were to:

  • Streamline claims verification and processing
  • Automate routine data entry and customer support tasks
  • Enhance fraud detection and improve overall response times

Our Approach

Process Audit & Analysis

Conducted a comprehensive review of existing claims processing and customer support workflows to pinpoint inefficiencies and error-prone areas.

AI Integration

Integrated natural language processing and machine learning algorithms to automate claims verification, data extraction, and initial customer support interactions.

Automation & Optimisation

Deployed AI-powered chatbots and predictive analytics to automate routine queries, reduce data entry errors, and flag potential fraud.

Challenges & Solutions

Claims Processing Delays

Manual verification and processing of claims led to prolonged turnaround times.

  • Time-consuming document review
  • High risk of errors

Solution & Results

Automated Claims Verification:

  • NLP-driven automation reduced processing time by 70%
  • Increased accuracy in claim assessments

High Data Entry Errors

Manual entry of claim details resulted in frequent mistakes and delays.

  • Inconsistent data capture
  • Repeated corrections required

Solution & Results

Automated Data Extraction:

  • Streamlined data capture reduced errors by 80%
  • Faster claims processing and reduced rework

Inefficient Customer Support

Slow response times led to customer frustration and poor service experiences.

  • High volume of routine queries
  • Overburdened support staff

Solution & Results

AI Chatbot Assistance:

  • Chatbots handled routine queries, reducing support load by 60%
  • Improved customer satisfaction and faster response times

Fraud Detection Challenges

Manual review processes made it difficult to promptly identify and mitigate fraudulent claims.

  • Limited analytics capabilities
  • High risk exposure

Solution & Results

Predictive Fraud Analytics:

  • Implemented machine learning models to flag suspicious claims
  • Reduced fraud-related losses by 50%

Results

Pre-Automation Total:

180 hours/week

Post-Automation Total:

60 hours/week