Automated insurance claim settlement enables insurance organisations to process claims consistently, accurately and quickly to dramatically improve the efficiency of processes and offer significant cost savings.
With automation already affecting the insurance industry in the areas of underwriting, claims and broking, big changes to claims settlement are certainly imminent.
How does the automation of claim settlements work?
Manual, error prone and cumbersome activities are often barriers to productivity, especially when claims professionals have to access information through multiple sources (personal data, geographic information, coverage details and policy information, health and medical codes and more) to make decisions and settle claims.
This is where cognitive technologies can play a critical role in improving process efficiency, where ease, speed, convenience of service and price are key competitive differentiators. Cognitive technology uses a human-like understanding of content to automatically access, identify and extract the relevant information in documents, forms, applications and policies and assist claims professionals with determining and finalising claims settlement.
Claims functions are often viewed as one of the most manual, labour-intensive parts of an insurance business, so let’s take a look at some of the benefits of automated claim settlement process:
- Better decision-making: Firstly, the accurate and faster responses to the automation of claim settlement have reduced the workload of policyholders and other organisations. Additionally, automation of claims drastically decreased the amount of rework involved and legal costs.
- Faster approval process: Secondly, claim settlement processing automation has led to quick and accurate processing of claims. The seamless extraction of the information by technology has quickened the approval process.
- Greater efficiency for validation processes: Thirdly, automation in each step – a collection of claim information and other supporting documents, mapping the relevant information against the policy coverage database, and other processes – makes the process of validation more competent.
What are the notable changes that automation of claim settlement processing brings in an insurance company?
- Better control of claims and improved communication, both internally and externally.
- Steep increase in customer retention and improved customer satisfaction rates.
- Simple claim requests handled by the automated mechanism.
- Integration with other tools and solutions enhances the overall business productivity.
- Enhanced standardisation of the claims settlement process.
According to a study by America’s Health Insurance Plan, automation of claims processing brings down the actual costs for paper processing by 50%. Other findings of the report include:
- Unlike the manual paper processing, which takes 2 to 3 months, claims settlement can be achieved in 7 to 14 days.
- Improvement in processing time by up to 85% and cutting half of the costs.
- Improvement in overall productivity and progress in other areas of operations like resource allocation, cash flow, and budgeting.
Automation has increased efficiency and decreased labour costs in insurance claim settlement processing and has the potential to completely transform the way we think about customer experiences in the insurance industry. That’s why GB recently appointed Mr Sameer Oghanna to the position Head of Automation, AI and Analytics for Australia and New Zealand.
GB is working to further our digital initiatives by driving automation, harnessing artificial intelligence, and embedding machine learning and analytics deeper within our business.
“Successful adoption will improve outcomes for our customers through more efficient and effective operational performance, and enable the discovery of new product lines for GB” said Mr Oghanna.
In summary, claims employees who once spent their days asking routine questions, filing paperwork and processing claims can now turn to automated process and machine learning that recognise patterns in large amounts of customer data, with the benefits including better decision making, faster approval process and greater efficiency for validation processes.