Claims processing has long been a cumbersome, labor-intensive, and error-prone process, with few opportunities to scale. From the over-reliance on legacy systems to the inadequacy of understaffed and overworked teams, the insurance industry has often struggled to keep up with the pace of change.
So, it doesn't come as a surprise thatAI-powered automated claims processing systems are becoming increasingly in vogue to solve such problems. Artificial Intelligence (AI), a field of computer science that deals with intelligent machines, drives these systems to streamline the claims handling process by analyzing and processing data, improving efficiency, reducing expenses, and enhancing claim accuracy.
In fact, analysts at management consulting giant McKinsey & Company project that claims handling will become a highly significant application area for AI, with AI-powered automation expected to help process more than 50% of the claims by 2030.
According to such projections, efficiency, precision, and customer satisfaction rates will be at an all-time high during the latter half of this decade. Policyholders will be pleased with having all their needs met on time, and cost-efficiency will no longer be an afterthought. Tens of thousands of claims will be filed every day, and data will be streamed in real-time to make adjustments at a moment's notice.
Indeed, the predictions can't be more precise about the massive adoption; however, the technological adeptness of automated claims processing solutions is already palpable. The pervasive nature of AI is all set to significantly optimize the claims handling workflow and develop the ability to handle thousands of cases at scale — all thanks to the combination of Machine Learning (ML), Optical Character Recognition (OCR), andNo-Code administration.
Let's take a peek at the most impressive ways AI-powered systems can help insurers take the frustration out of claims handling.
Going Beyond OCR
The use of optical character recognition (OCR) has been a common practice since the advent of digital imaging. OCR is utilized to convert barcodes, prices, and other textual data into text that can be further processed for multi-faceted functions. In the context of claims handling, it is most often used to automate the process of scanning, converting, and indexing receipts and other documents.
While OCR has been instrumental in speeding up the claims processing workflow, it's not infallible. The algorithms aren't meant to handle noisy images and other stylizations.Besides, OCR doesn't possess the ability to extrapolate meaning from a given text. To identify document-specific fields and images, object recognition is used to fill the gaps.
ML and AI are now capable of surpassing OCR by using all signals from a document to understand and identify the meaning of text on the page, not just the characters. This development enables the systems such as AutoExtract.ai to leverage intelligent information extraction, categorizing documents and extracting textual information based on all possible signal in a document.Enhanced data extraction models can understand the concept of “policy number”or “policy holder name” and identifying it on claim documents from different sources and formats.
AI's ability to learn and develop based on the existing data has made it possible to streamline a slew of processes across the industry. Think of it as a self-learning system that's programmed to handle a specific task, except it doesn't require explicit programming and learns while on the job.
In the current context, this translates to the claims processing system learning from ongoing workflow and refining the functionality, all while remaining in the background.As such, users don't need to worry about programming and training models and can leverage the AI-powered solution as a plug-and-play tool.
87% of the policyholders surveyed by EY cited speed, accuracy, and process transparency as the top factors influencing their insurance decisions. For most of them, the insurance purchasing process is dictated by a predominant criterion — a clear and prompt policy summary.
Imagine having to manually review multiple claims for the primary reason of verifying for the policy number. Such a process would be time-consuming and probably quite frustrating.
AI can streamline this workflow and bring down processing time substantially. AI-powered solutions can perform a host of core insurance-related tasks like validating policyholder identities and their coverage status, verifying the validity of claims, and autonomously reviewing payments once the formality checks are completed.
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