Streamlined systems improve health insurance customer service

At a glance

We’re helping a tier-one Australian health insurer use automation technologies to reduce manual touchpoints across workflows, enhance the automation of claims processing, and improve accuracy and processing times for improved customer service and experience.

Key platform: AWS
  • AWS Lambda
  • AWS SQS
  • Amazon Textract
  • Custom machine learning model from Mantel Group

Automation drives
better service

A tier-one Australian health insurer is undertaking a strategic initiative to consolidate multiple insurance systems and platforms of record into a single, streamlined solution while enhancing its core capabilities.

A key component of this simplification strategy is leveraging automation technologies and modern software architecture patterns to minimise manual touchpoints across workflows.

One critical area of focus is enhancing the automation of the claims process. This project involved the adoption of intelligent document scanning technology to improve accuracy, accelerate processing times, and reduce manual intervention, contributing to the broader goals of operational excellence and improved customer experience.

As part of a strong relationship with this client stretching back 6+ years, Mantel Group delivered two key streams of work through the engagement, each designed to address specific objectives and requirements.

Stream 1

Infrastructure platform

This focused on building the core infrastructure and machine learning (ML) platform to support the Large Language Model (LLM) and Optical Character Recognition (OCR) components. This initiative established a scalable and secure environment capable of handling real-time OCR processing and decision-making based on predefined rules.

Stream 2

Enhancing OCR accuracy

This addressed the immediate need for straight-through processing, a critical requirement for success. This effort involved evaluating and optimising the existing OCR rules, analysing a dataset of invoices, and validating LLM options. The goal was to enhance OCR accuracy and increase automation levels to streamline operations effectively.

The solution is now live in production, and the health insurer has observed a significant increase in straight-through processing leveraging LLM technology for their claims process, surpassing the performance of the previous solution.

This advancement has reduced manual intervention by operators while enhancing the accuracy of the claims process. Moving forward, further optimisation efforts will be undertaken to refine the solution and minimise manual handling even further.

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