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AI's Potential to Optimize Health Insurance is finally being realized. What’s driving the change?
Claudio Caula
With advancements in artificial intelligence (AI) implementation, manual and time-consuming health insurance claim approvals could soon be a thing of the past.
The speed and efficiency of health insurance claim approvals are important to insurers. When customers are financially and physically vulnerable, the time taken for medical costs to be refunded to them can influence their satisfaction levels. Driven by such needs, insurers have actively explored new ways to automate and accelerate the claims approval process.
There have been attempts to implement AI into the claim approval process, but it can be a challenging task. Insurers have found the claims approval process difficult to automate as it requires complex information - such as medical invoices and lab reports - to undergo a thorough assessment process which requires deep medical knowledge. There are also concerns from insurance leaders over the impact of AI on the strength and relevance of their workforce.
Recently, there has been renewed interest to use AI to accelerate the claims approval process. With the right suite of AI and machine learning solutions driven by human insights, insurers are now able to automate the end-to-end claims process electronically without any manual intervention and still achieve a high level of accuracy.
So, what has changed to make this breakthrough?
1. Improvement of the technical infrastructure
More insurers are adopting cloud applications, which has made AI solutions faster and more cost-effective to execute.
With cloud-hosted applications, Application Programming Interfaces (API) can be quickly integrated into current enterprise systems to automate processes. The cloud also allows the implementation of data analytics platforms and Machine Learning Operations (MLOps) to facilitate the smooth creation, maintenance and tracking of AI applications.
If done right, a cloud-based AI solution can seamlessly execute APIs and provide results in seconds.
2. Better image recognition solutions and enhancements in AI results
The use of AI has also advanced image recognition technology to a point where assessors can quickly receive accurate and usable text data from most images.
The first step in the claims process is to manually input claims information for further assessment, which is submitted as forms, medical reports, and medical receipts in image formats. However, the lack of standard layouts across medical service providers and tabular financial data formats have resulted in image recognition technologies producing dismal output results.
Thanks to advancements in AI and machine learning, image recognition technologies can now achieve impressively accurate results. In fact, image recognition technologies used by major insurance players such as AIA have managed to achieve an output accuracy of above 90%.
3. Digitizing Customer Interactions
The abilityto submit claims digitally has also breathed new life into AI applications.
Not long ago, all forms and documents were submitted as physical documents that required a person’s intervention, as crucial data would need to be manually input into systems.
Today, customers simply need to type in information and upload photos of the required documents online. With crucial information available in digital formats, it removes the need for in-person intervention and makes it easier to automate the remaining process viaAI solutions.
4. Change in the organization culture
Organizational cultural changes have led to more successful AI solution implementation
Changing the way organizations work is always challenging, especially for processes like claims approval, which have been practiced a certain way for decades. Claims assessors were initially cautious of leaving important decisions to a machine. It is not a surprise that in many organizations, attempts to use AI for claims approval often fail due to human resistance.
Recently, there has been Renewed Interest to use AI to Accelerate the Claims Approval Process
More insurers are beginning to educate employees about the reliability of data analytics, which has contributed to the acceptance of AI solutions. Such programmes help employees better understand how AI solutions work, and how to manage the risks involved. This also assures employees that AI can improve their work performance instead of replacing them.
Conclusion
With advancements in the people and technologies powering AI, the technology is now ready to make its mark on the insurance industry.
For example, the implementation of AI solutions in AIA has contributed to time savings and customer experience improvements. AIA has managed to automate 90% of their claims approval processes while achieving an output accuracy of 99.7%. This has reduced the time required to process claims from 2 days to 2 minutes – whichmeans that customers can expect a refund in their bank account in less than 20 minutes upon submitting a digital claim. Having seen the advancement of new AI models (i.e. Transformers) with big improvements in the fields of Computer Vision and in Natural Language Process (NLP), we are already exploring the usage of those methodologies, and can soon expect further improvements on the accuracy of AI solutions to automate the claims approval decisions.
AI’s potential to optimize the health insurance process is ready to be realised. and this presents the industry with a good opportunity to drive AI adoption. By delivering accurate, efficient and speedy processes at a fraction of the time and effort, AI is here to move the industry forward.
