by Navin Gupta, on Jan 30, 2023 7:21:04 PM
Estimated reading time: 4 mins
Key takeaways from the blog- AI/ML models transform the Insurance Claim Adjudication space.
- They have to potential to automate the Insurance value chain.
- They streamline the Claim Processing and accelerate the process.
Insurance Claim Processing is an effort-intensive process. With many touchpoints, including receiving the first notice/claim intimation, creating a case folder, receiving the documents in order, reviewing them, adjudication, settlement, explanation of benefits/remittance advice, and reimbursement, it is a tedious workflow. Of these multiple sub-processes, only a few have been brought under the scope of automation, for example, first notice and explanation of benefits. However, the majority of the points follow regular manual processing. As a result, the entire Claim Processing cycle takes a long time for fulfillment. Claims Adjudication is one such human-dependent and lengthy manual process. Automation improves processing efficiency and cycle time. With each Claim running into a few megabytes to gigabytes and consisting of unstructured data in various formats, AI/ML-powered automation, which extracts the required data and processes it further, is a must-have.
How is automation leveraged in conventional Insurance Claim Processing?
First intimation and explanation of benefits (EoB) are the two prominent insurance claim processes already in the ambit of automation. These two processes leverage Intelligent Automation to a particular extent to accelerate Claim Processing. First intimation uses a digital platform accessible on any device, anytime, anywhere. EoB automation parses data from multiple sources and organizes it in a standard format for payment processing/reimbursement and archival/referencing.
What is Claim Adjudication?
Claim Adjudication is the process of receiving the Claim documents from the insured entity/person, assessing the Claim, comparing it with the terms and conditions in the Service Level Agreement (SLA), and manually deciding whether to honor the Claim and in what proportion. The process generates multiple calculations and analysis documents at various levels. At the end of the Claim Adjudication process, all the final calculations are noted along with reasons in the Explanation of Benefits (EoB) or Remittance Advice. This advice is crucial for keeping track of why the payment was made or withheld, along with the specific reason and endorsement of the adjudicators/reviewers.
How does AI/ML enable Insurance Claims Adjudication?
Claims Adjudication/Processing includes checks at different levels. Intelligent Document Processing and Intelligent Automation parses the unstructured data received in the format of handwritten documents and the video/pictures and ingests and integrates it with the downstream processing systems. Pre-trained AI/ML Claim Adjudication Models capture the tacit knowledge of the adjudicators/decision-makers to build the recommendation Claim Adjudication engines that enable seamless Claim Processing/Management. These AI/ML models sieve through the hundreds and thousands of claims received almost daily. With the built-in jargon and their synonyms related to the different insurance categories, the engine processes the Claim Adjudication and reduces the turnaround time from days to minutes. The models get stronger by handling higher volumes and through each exception handling.
AI/ML Claim Adjudication models improve the experience of both the adjudicators and the customers/insured. Automated Claim Adjudication can be used for both low and high-cost insured entities/items. It results in significant improvement of the overall Insurance Claim Processing. The insurance providers can pass on the savings accrued in the operational costs to the customers through reduced premiums or bonuses.
Benefits of AI/ML-powered Claim Adjudication models
- Reduce exclusive human-dependence: The tacit knowledge that goes into Claim Adjudication is captured in the AI/ML models. The models help the human counterparts to process the claims at a faster pace.
- Reduce paper-work: The automated process uses digitized versions of the claims that are easy to store, process, and access in a role-based environment. As a result, the once paper-intensive process transforms into a paperless function.
- Optimize decision-making: AI/ML sums the entire process of adjudication and decision-making in a few algorithms. It enables insurance providers to accelerate the processing of the overall Insurance Claim Processing from days to minutes.
- Eliminate fraud: The automation reduces instances of false positives and false negatives. It eliminates fraud even through the accelerated pace of processing. It improves customer satisfaction of the genuine insureds and penalizes the bad actors.
- Streamline processes: It automates to streamline the Claim Adjudication and influences the overall Claim Processing cycle and turnaround, reducing it from days to minutes.
- Automation of tacit knowledge: It ingrains the adjudication logic and undocumented decision-making/practices into the training of the AI/ML algorithms. It addresses issues resulting from decision-maker churn.
- Pretrained jargon models: It builds in the Insurance industry jargon into the AI/ML models that enables processing handwritten forms with higher accuracy.
- Prevent delays: It eliminates delays associated with manual Claim Processing that tantamounts to harrasments of the insureds.
- Automate the Insurance value chain: AI/ML models have the potential to automate all the milestones of the Insurance value chain. It includes process optimization, underwriting, fraud detection, policy servicing, risk profiling, and risk management.
In summary
Pre-trained AI/ML models automate the tedious and lengthy Claim Adjudication. The models handle vast amounts of claims and produce more accurate results with time and higher volumes processed. The models get stronger and more intelligent with each exception handling. They eliminate fraud from the entire value cycle even while speeding up the Claim Processing.
