This article is a deep dive into insurance claims automation: What is claims automation, really? We’ll walk through how it works, why it matters, and how insurers like Tokio Marine are already seeing millions in savings by plugging claims leakage with smarter automation.

You’ll also find helpful links to blog posts, product info, and real-world case studies if you want to go deeper. It answers key questions insurers are asking about what it is, how it works, and why it matters. You’ll also find helpful links to blog posts, use cases, and case studies for further reading.

Insurers like Tokio Marine & Nichido Fire are already saving millions each year by reducing claims leakage through smarter, automated processes. Others are cutting loss adjustment expenses with better FNOL and downstream workflows.

Here’s what we’ll cover:

• What is automated claims processing?
• What is the automation process in insurance?
• What are the 4 phases of the claim process?
• How do insurance companies process claims?
• How do you automate claims processing?
• Do insurance companies use AI to process claims?
• How can AI be used in claims processing?
• How is AI used in insurance claims?

Automated claims processing is exactly what it sounds like: using technology to handle claims automatically without requiring humans to touch every step. Instead of sending a claim through a chain of people and spreadsheets, automation can:

• Verify information instantly
  
• Trigger workflows based on data
  
• Flag anomalies for human review
  
• Settle low-risk claims with straight-through processing
  

It’s faster, more accurate, and scalable. That’s why it’s the go-to move for modern insurers looking to boost efficiency and customer experience.

Automation in insurance isn’t just about “set it and forget it “ workflows that allow workers to lean back while the robots do their job. 

Instead, it’s about replacing outdated workflows with smarter systems and creating more efficient processes from end to end, reducing errors, and making organizations overall more efficient with time and money. This is the core of automation in insurance: the use of technology to handle tasks traditionally done by humans, reducing manual errors, speeding up processing time, and improving consistency.

What is automation in insurance? The automation process usually includes:

What is an example of process automation in insurance? 

One common use case for insurance automation is a claimant submitting a photo of a damaged vehicle through a self-service portal. The system can instantly confirm policy coverage, estimate the damage, check for fraud, route to a human adjuster if needed, approve a payout, and close the claim — all without human intervention.

Claims automation doesn’t erase the need for a strong procedural foundation. The process, regardless of line of business, still follows these four main phases:

1. 1. Claim Intake or FNOL (First Notice of Loss): This is the first stage, when a policyholder initiates a claim and the initial loss data enters the system. Automation ensures accurate, complete data capture from the start and eliminates the delays and errors that come with manual entry.
   2. Investigation: This phase involves verifying the details of the claim, including coverage, circumstances, and potential fraud. With automation, insurers can use data validation tools and AI to speed up the process and flag inconsistencies or suspicious claims for further review.
   3. Evaluation: During evaluation, insurers assess the extent of loss or damage and determine the payout amount. Machine learning algorithms can analyze the data, estimate costs based on historical patterns, and recommend a resolution path, reducing human bottlenecks.
   4. Settlement: The final step is issuing payment to the policyholder. Automation allows for straight-through processing for simple claims, integrates with financial systems, and ensures timely, accurate disbursements.

Each step in the claims processing journey gets faster, more consistent, and more cost-effective with automation in place.

Claims processing automation starts with rethinking what work humans should do — and what machines can handle better. This shift is at the core of smart claims automation processing. Some core insurance automation ideas include:

This approach is closely aligned with the broader concept of IT process automation. IT process automation refers to the use of software to automate repetitive, rule-based tasks and workflows within an organization. In insurance, this could mean everything from automated claim assignment and status updates to real-time risk scoring and fraud detection — all of which can reduce manual workloads and shorten processing times.

To begin automating claims effectively, insurers should:

1. 1. Map existing workflows to identify manual chokepoints and inefficiencies.
   2. Prioritize use cases based on ROI — for example, claim intake, FNOL, fraud detection, or low-risk claim settlement.
   3. Build integrated systems that connect policy, billing, claims, and CRM platforms.
   4. Establish rules and logic that handle common claim types without adjuster involvement.
   5. Introduce machine learning where needed to improve decisions over time.
   6. Pilot and test before scaling automation more broadly across claim types and regions.

ClaimCore® is one example of how EIS supports this kind of transformation — but the overall playbook is relevant for any insurer ready to automate. If you still rely on manual inputs, emails, or spreadsheets to move a claim along, the gap between where you are and where your competitors are heading is only getting wider.

AI in insurance claims processing is becoming table stakes. In fact, many insurers are using or experimenting with some form of AI to reduce friction, flag risk, and accelerate resolution.

While workflow automation is incredibly beneficial to claims departments, AI brings value beyond what even that can provide.

Traditional automation routes and executes tasks, but AI adds intelligence by recognizing patterns, learning from outcomes, and making context-aware decisions. The result is even faster claims processing and better fraud detection.

Common AI tools used in insurance claims include:

• Natural Language Processing (NLP): To extract relevant information from claim descriptions, emails, or chat logs — eliminating the need for manual interpretation.
• Computer Vision: Used in auto and property claims to evaluate photo or video submissions, assess damage, and recommend next steps.
• Predictive Analytics: To score claims based on likely cost or fraud risk — helping prioritize workload.
• Machine Learning: To continuously improve decision accuracy and risk models based on new data.

In one real-world case, an EIS client used this kind of smart automation to reduce claims leakage and speed up payouts. Here’s how they did it: The Smart, Automated Way to Reduce P&C Loss Adjustment Expenses

Whether it’s fraud detection, triage, or self-service enablement, AI is reshaping what insurers can do — and how fast they can do it.

How else can AI be used in claims processing?

This is where AI moves from concept to real value. It doesn’t just support the claims process — it transforms it. Let’s build on the earlier example of an EIS client who used AI to cut loss adjustment expenses and speed up claims: they integrated automated fraud detection and predictive risk scoring into their workflow. The result was faster triage, fewer manual reviews, and far more accurate payouts.

Here’s a more detailed breakdown of how AI shows up in real-world claims environments:

• Predictive Analytics: These tools use historical data to anticipate claim severity, duration, and cost. For example, as soon as a claim is filed, predictive models can identify whether it’s likely to escalate or be low-impact. That helps route it to either straight-through processing or a human adjuster right away — saving time and resources.
• Computer Vision: In property and auto claims, images submitted by claimants are analyzed automatically. AI can identify damage patterns, compare them to previous cases, and generate an estimated repair cost. This allows insurers to avoid slow manual appraisals and reduce estimate discrepancies.
• Chatbots & NLP: Natural Language Processing powers digital intake tools that can guide a claimant through FNOL, interpret written statements, and extract structured data from unstructured text. This eliminates the need for manual entry and reduces clerical errors. AI chatbots also respond to claim status inquiries or missing document alerts in real time.
• Machine Learning: ML algorithms detect evolving fraud patterns, identify anomalies in behavior or claim frequency, and refine risk scoring over time. For example, if a specific combination of data fields consistently results in flagged or denied claims, the system learns and adjusts risk assessments automatically.

Together, these tools don’t just make claims faster — they make decisions smarter, reduce leakage, and improve customer satisfaction. In short: good AI doesn’t replace people. It puts better tools in their hands so they can focus on complex decisions, not paperwork.

Beyond just automation, AI is transforming the role of the claims adjuster. It’s not about replacing them — it’s about giving them better tools and more time to focus on what matters.

• Will AI replace claims adjusters? No. But it will drastically reduce the volume of repetitive tasks they deal with daily — freeing them to handle high-severity or complex claims that truly require a human touch.
• AI claims solutions like EIS ClaimGuard™ detect fraud in real time using dynamic risk scoring. Tools like ClaimPulse™ automate claimant updates and keep communication flowing without any manual intervention.

Let’s look at how this plays out across different claim types:

P&C Auto Claim Scenario Using AI

1. 1. FNOL: A customer submits damage photos and basic details via a mobile app. NLP tools parse the customer’s input, and computer vision scans the photos.
   2. Triage: Predictive analytics score the claim’s severity and risk. Low-risk, low-value claims go straight to auto-pay workflows. Higher-risk claims are routed to human adjusters for further assessment.
   3. Investigation: Machine learning models compare the claim against prior patterns to spot anomalies for fraud detection. If a risk flag appears — like a location mismatch or repeated claim behavior — it’s escalated.
   4. Estimation & Settlement: Computer vision suggests a repair estimate. If the claim amount falls within given parameters, the claim is approved and paid without adjuster review.

Group Benefits Disability Claim Using AI

1. 1. Claim Intake: An employer or employee submits claim information through a self-service portal, where that data is validated in real time against eligibility and policy rules.
   2. Verification: AI tools pull in employment and medical information, then score the claim’s legitimacy using structured and unstructured data (like doctors’ notes).
   3. Ongoing Management: Claim status, documentation requests, and reminders are all automated. AI-enabled tools can handle claimant outreach with event-based messaging, making sure all parties are aware of additional information needed, claim status, and next steps.
   4. Return to Work & Closure: ML models can predict return-to-work timelines and alert claims teams to any anomalies that might be happening with a claim, such as unexpectedly extended durations so the claim can either be investigated or closed efficiently.

When you combine human judgment with AI precision across these steps, claims move faster, customer satisfaction rises, and leakage drops. This isn’t theoretical — it’s what modern insurers are doing right now to stay ahead.

Insurance claims automation isn’t about hype — it’s about execution. Throughout this article, we’ve broken down what it really means to modernize the claims process and what kind of results insurers can expect when they do it right.

You’ve learned:

• What claims automation actually is — and what it is not
• The phases of a claim and how automation fits into each one
• The inefficiencies in traditional, manual claims workflows
• What a modern, automated claims process looks like in real-world scenarios
• Smart insurance automation ideas insurers can apply right now
• What IT process automation is and how it powers more intelligent claims handling
• How to build and launch a claims automation strategy that sticks
• Where AI fits into the broader automation landscape — and how it enhances rather than replaces
• Detailed examples of AI in action for both P&C and group benefits claims

If you’ve made it this far, you now have a full understanding of what claims automation in insurance involves, why it matters, and how to start or expand your journey.

That’s exactly what EIS helps insurers achieve with our claims management and claims automation & fraud detection solutions.

If your systems aren’t up to the task, your competitors’ will be. It’s time to automate smarter, so you can compete more strongly in the marketplace. For more information, check out any of the links in this article, or book a call to discuss what EIS claims solutions can do for your business.