How AI Is Transforming Claims Processing in Insurance
Build a New Way of Insurance with AI Claims Processing
There's no way around it: AI has arrived in insurance. And experts are labelling AI's impact in insurance claims a "seismic" shift.
But how does AI for insurance claims processing work?
As it stands, claims teams juggle mountains of paperwork, complex legal evaluations, and time-consuming investigations. This frustrates claimants, bogs down claims adjusters, and increases claims-processing costs.
Clearly, AI for claims processing can help—but there's too much at stake in insurance, like regulatory compliance and even claimants' livelihoods, to invest in an insufficient solution.
This article will identify the challenges of manual processes & generic AI tools and overview different types of AI for insurance. We'll also summarize the benefits of AI-based claims processing and discuss the impact of AI in insurance claims.
Knowing how AI is transforming insurance can translate into you implementing AI to transform insurance yourself, enabling your teams to do what they do best, helping people.
The Burden of Manual Claims Processing
Anyone who's ever reviewed an insurance claim knows the feeling of seemingly endless paperwork. Analyzing medical documents, workplace reports, and more is exhausting.
That workload isn't going away soon. Research shows that insurance-claim volumes are increasing. And the data is clear: a caseload over 111 per claims adjuster diminishes returns—though almost half of all adjusters have caseloads in excess of 125!
Ultimately, manual claims processing is highly resource-intensive. Consider these pain points:
- Time-Consuming Tasks: Claims adjusters spend hours or even days sifting through documentation, following up on missing information, and manually analyzing complex claims. This leads to delayed claim resolutions and dissatisfied policyholders.
- Prone to Errors and Oversights: Humans can make mistakes, especially when overburdened. The more complex the claim, the higher the likelihood of costly errors or missed details.
- Drains Creativity and Innovation: With repetitive data entry and tedious document reviews, there's less time for strategic work. Supporting genuine claimants and efficiently catching fraud for ideal outcomes takes human creativity and intuition.
And these manual processes hurt insurers' bottom lines too. The average claims leakage for insurance providers is 8–12%, far below the best-in-class standard of 2–3%.
Overwhelmed by endless insurance documents and unmanageable caseloads, claims adjusters & investigators and legal teams feel the pinch. For insurers, it means increasing costs and diminishing customer trust.
AI for claims processing is the way forward, but choosing the right AI claims solution is paramount. Fully automating the claims process, using inadequate technology, and replacing humans can be even riskier than sticking with manual processes.
Let's talk about what AI for claims processing actually looks like—how the technology works, what the different options are, and how insurers can benefit from smart solutions.
How Insurers Use AI for Claims Processing
First, if AI transforming key insurance functions—how? Let's understand how claims departments at insurance carriers can leverage AI solutions.
1. Claims Management
AI frees workers from routine tasks like processing claims documents and provides insights to claims teams to help them make better & faster decisions.
AI-based claims processing can scan large volumes of medical or legal documents quickly and analyze the claims data for useful insights, helping teams discover and research important information to save time and better support claimants.
2. Fraud Investigation
Fraudulent claims are costly for insurance providers, but AI can drastically mitigate fraud. By identifying patterns indicative of fraud, AI can flag suspicious claims and help investigators focus on high-risk cases, preventing losses and reducing unnecessary backlogs.
3. Legal Teams
Insurance claims often require reviewing complex contracts, medical records, and legal details. AI simplifies these processes, summarizing key information and linking with explanations to relevant documents for citations.
When legal counsel gets involved in an insurance claim, equipping them with AI helps them focus on what they do best, using their intuition and know-how to make swift decisions with confidence.
Types of AI for Claims Processing
AI is not a one-size-fits-all solution. Different types of AI serve distinct purposes, particularly when applied to insurance claims processing.
Below are different types of AI for insurance claims. Let's delineate them to understand what exactly we mean by "AI for insurance."
- Predictive AI
- Agentic AI
- Generative AI
- Deterministic AI
- Document-Processing AI
Predictive AI
Predictive AI analyzes historical data to forecast future trends or outcomes. For example, AI will "predict" claims outcomes by looking at data in the aggregate from past claims decisions to make so-called best guesses.
While useful for market forecasting, it is less effective for insurance claims as predictions can miss nuance and context.
Specifically, the risks with predictive AI include inaccurate decisions, unfair discrimination, and a lack of transparency.
Agentic AI
Agentic AI can sometimes be predictive, so it overlaps with predictive AI. But what uniquely defines agentic AI is when an AI "agent" stands in for a human employee, from claims adjusters to customer-service chatbots.
However, these systems can overstep when automating delicate decisions without oversight, such as determining claim payouts. It's not just claimants who suffer, but insurers risk stiff compliance violations if things go awry.
And we all know the frustrating feeling of dealing with AI agents. Indeed, 83% of consumers are wary of agentic AI in insurance.
Generative AI
Generative AI "generates" outputs based on input prompts. A popular example is ChatGPT, which provides output text or data based on users' input queries.
Generative AI benefits claims processing by generating insights about a claim. Owl.co's generative-AI tool, OwlAssist, enables claims teams, fraud investigators, and lawyers to ask questions about a claim for real-time information to make fast & accurate decisions.
For example, claims adjusters can ask OwlAssist to uncover medical or workplace information, liberating them from digging through paperwork. And users can provide feedback for the AI to constantly improve its own accuracy and relevance.
Teams can also leverage non-textual generative AI for insurance to create visual data, like body-part diagrams or earning-potential graphs, or insurance document generation to produce any type of document, like summary reports of doctor visits.
Deterministic AI
Deterministic AI is rules-based, focusing on evidence, not predictions. It functions on predefined logic to analyze insurance claims independent of other claims.
Unlike predictive AI that relies on patterns from historical data, like when predicting a claimant's injury-recovery timeline, deterministic AI provides accurate insights based on the claimant's actual medical information.
Benefits of deterministic AI, which powers Owl.co's insights tool, OwlSignal, include reliability and transparency. OwlSignal, for instance, prioritizes complex claims or flags potential fraud cases without risky algorithmic bias.
A key factor of AI for insurance claims is trust. How can claims teams trust AI's accuracy? Incorrect or misleading insights could lead to missed fraud or false positives.
AI for claims is trustworthy when it can explain itself. This is how you get explainable AI in insurance; insights have citations or evidence. Deterministic AI accommodates explainability because it’s rules-based.
Document-Processing AI
Claims adjusters, fraud investigators, and in-house lawyers have to handle lots of documents, causing processing backlogs and exacerbating claims leakage.
Document-processing AI reads & interprets data and uses AI document classification for insurance, enabling staff to focus on their uniquely human critical-thinking skills.
What's key is when document processing integrates with deterministic & generative AI for a holistic toolkit designed specifically for insurers. Generic AI solutions risk misinterpreting context unique to insurance claims, jeopardizing the accuracy and relevance of claims insights.
That's why the best AI for reading insurance documents isn't really "document processing" but is called "document understanding"—AI for claims processing is only as beneficial as the AI's ability to fully understand unique details.
OwlVision, for instance, Owl.co's document-processing tool, leverages a custom vision-language model for best-of-class accuracy rates in understanding both structured & unstructured data.
A bespoke vision-language model for ingesting documents is critical for AI-based claims processing because it combines textual & visual capabilities to understand document nuances.
This differs from some popular AI technologies that primarily depend on OCR (optical-character recognition). OCR is great for reading text but is vulnerable with messy handwriting or poor-quality document scans.
OwlVision uses OCR—but only to the extent that it's reliably accurate. Otherwise, our AI is trained to look at the context of documents to optimize precision, providing a bedrock of objective data that integrates with our enterprise-AI solution for insurance.
Why AI for Claims Processing Matters
AI-enabled claims processing is not about automation for automation's sake. It's about meaningfully changing in how insurance companies operate and deliver value.
AI that provides accurate and responsible insights empowers insurance leaders to become corporate stewards, enables claims teams to deliver fairer & faster decisions, and futureproofs enterprise organizations to help policyholders.
Let's unpack these reasons why AI for insurance claims processing is impactful.
1. Empowering Insurance Leaders
AI-based claims processing that focuses on facts with precision empowers insurance leaders to transform their industry.
AI based on precise knowledge enables accountability, functions effectively, and operates ethically, thus inspiring insurance leaders to create new ways of doing business.
2. Enabling Claims Workers
Will AI take over adjusters? It can—but it shouldn't. And it doesn't have to because AI that provides accuracy instead of guesswork enables humans to use their intuition to make complex decisions, ultimately outperforming AI-only claims processing.
AI eliminates manual labor so experts can focus on high-stakes decisions requiring human intellect.
Far from supplanting human expertise, AI uplifts frontline claims professionals, reducing workloads, and freeing space for more creative, impactful work.
3. Futureproofing Organizations
The insurance industry is evolving rapidly, with changing regulations and increasing customer expectations. Implementing human-centric AI that's accountable, effective, and ethical ensures compliant and responsible business growth.
Insurance leaders who implement change that drives faster & fairer decisions to reduce fraud and support policyholders create the opportunity for themselves to get the appreciation they deserve and become corporate stewards, on top of securing their company's future.
AI Claims Processing with Claims Intelligence
AI for claims processing that uses generative & deterministic AI with precise document understanding creates change—change that improves the bottom line, reduces fraud, and supports people.
Because it's fair that most people are apprehensive of automated digital experiences in insurance claims—they want humans. That's why insurance leaders need AI that's accountable, effective, and ethical to optimize claims processing.
AI that processes insurance claims based on real knowledge, not imprecise document understandings nor estimations from past data, is called Claims Intelligence, and it's how leaders can transform insurance.
To learn more, book a demo of Owl.co's AI-powered Claims Intelligence toolkit for enterprise insurers.