Quality Assurance for Insurance — Getting Automated Claim Adjudication Right in the First Attempt
By: M. Anas, Miles
Tags: Ai For Healthcare, Insurance, AI, Artificial Intelligence, Insurance Claim

Despite many attempts to improve first-pass auto-adjudication, claim errors remain one of the most significant sources of cost.

The U.S. suffers an approximate 275 billion loss every year due to fraud and other issues related to claims accuracy and administration. (Source: Centers for Medicare & Medicaid Services)

Why is QA Assurance for Insurance so Challenging?

The issue with the QA assurance for insurance is not about implementing new technologies or deploying new tools. It is even not about adopting the right approaches.

The problem is Test Coverage:

Even though human tester can create test cases, they can’t provide the required test coverage. There is a high chance that the false positive or false negative cases will go unnoticed.

Whether it is a top-down, bottom-up, or functional-analysis approach, each of them or a combination provides some testing coverage. However, this level of planning and execution is often skipped in the interest of time by the organizations. They neglect the coverage plan, without which much of the test coverage is skipped, resulting in error-prone testing.

As a result of the poor performance of QA assurance for insurance, no one expects it to work. They assume that they will get mediocre results and are ready to accept the cost of recovery as a standard cost of doing business, instead of improving test coverage.

Is A.I.-Powered Testing a Solution to the Woes of QA Assurance for Insurance?

To get better results in the QA Assurance for Insurance, there is a need to focus on the basics like:

Clearly-defined test objectives with comprehensive test coverage planning

The utilization of well-prepared test data

Functional analysis of the objects and systems

For optimum utilization of data and comprehensive test coverage, machine learning and artificial intelligence testing services can do wonders. A model-based testing tool, which generates intelligent software testing procedures and scenarios automatically using models of system requirements, can drastically increase test coverage. Similarly, a process accelerator can transform insurance companies’ QA and testing practices by assessing processes, procedures, tools, skills, and risks.

The regression optimization tools can further enhance the accuracy of the tests. They automate the collation of the dependent test cases/scripts-based on the changes. The predictive analytics tools utilize machine learning algorithms to leverage the data available to predict the results of a case and display results in the form of graphical dashboards. It helps insurance businesses in decision-making.

Conclusion

Healthcare insurance needs automation testing that does away with most of the limitations of manual testing, which includes less coverage for test cases. However, the true potential of the massive amount of the data available with the healthcare service providers can only be leveraged with a testing approach that involves the use of artificial intelligence and machine learning.

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