The traditional claims process is riddled with inefficiencies, outdated manual workflows, and the ever-present risk of human error. This can easily lead to poor customer experiences, increased costs, and unnecessary claims leakage. In fact, 68% of complaints received by the National Association of Insurance Commissioners stem from claims issues.

That’s where claims processing automation comes in, handling and accelerating tasks that once required manual intervention. Examples include fraud detection, policy verification, damage assessments, and payments‌ — ‌all completed with greater speed and precision. And today’s ambitious insurers aren’t just using insurance claims automation to speed things up. They’re rethinking claims from the ground up with AI, machine learning, and real-time data integration. 

When insurance industry veterans ask, “What is automated claims processing?”, we can honestly say it’s not about removing the human touch from the process. It’s about getting rid of friction that slows down resolutions. An automation-powered claims system can cut insurers’ cost per claim by 30%, reduce fraud-related losses, and improve customer satisfaction scores.

By adopting automated claims processing, insurers can improve efficiency and reduce costs while also meeting the growing demand for seamless, digital-first experiences. Insurers using claims automation aren’t just keeping pace with industry expectations. They’re setting the standard for faster, fairer, and more accurate claims resolutions.

Claims processing is the end-to-end function insurers rely on to validate, adjudicate, and settle customer claims.

It starts when a policyholder reports an incident. From there it moves through coverage checks, documentation gathering, investigation, decisioning, and finally paying the claim or explaining a denial. Each stage has dependencies—a clerk, adjuster, auditor, or legal team must touch or review aspects of the claim before it can advance.

Traditional methods rely heavily on manual steps and siloed teams:

• Delays: Paperwork shuttles through underwriters, adjusters, audit teams, and clerks. Each move adds daily queues and causes bottlenecks.

• Errors: Manual data entry invites typos and misclassifications. A piece of data entered incorrectly can lead to an incorrectly denied claim, for example.

• Inconsistency: Subjectivity creeps into payout decisions and fraud suspicions. Two adjusters handling the same case could potentially reach different conclusions.

• Customer pain: Long wait times, opaque processes, and multiple handoffs can be frustrating. Policyholders wait on hold, submit documents repeatedly, and never know when their claim is moving.

These friction points lead to cost overruns, compliance issues, churn due to customer dissatisfaction, and reputation damage. That’s what automated claims processing seeks to replace with intelligent, consistent, and transparent workflows.

With EIS OneSuite, claims processing isn’t just digitized, it’s re-architected. Every stage is supported by real-time data updates, configurable rules, and smart workflows so carriers can shift from slow-moving claims queues to quick, accurate settlements with higher customer satisfaction ratings.

At its core, the claims processing workflow follows classic steps:

1. First Notice of Loss (FNOL)

2. Validation and coverage check

3. Documentation & further data collection

4. Adjudication and decisioning

5. Settlement and payout

Each step involves specific actors and triggers. Done manually, that means phone calls, emails, forms, phone tags, and deadlines, and can take days, weeks, or even months in extreme cases.

Manual workflow

• The cCustomer files claim by phone or email. This triggers an intake specialist who opens a ticket, creates a record, and enters basic policy data.

• A customer service rep (CSR) or adjuster enters data manually, like policy number, incident details, and key contacts. Mistakes here carry downstream.

• Adjuster requests documents, schedules inspections, and takes photos. This often requires multiple follow-ups.

• Internal team members review estimates. The claim moves through back-and-forth review and approval.

• The payment team initiates disbursement once approval is granted. That may involve several handoffs, system entries, or manual check runs.

This results in long cycles, high touch, and inconsistency. It also drives cost: adjusters spend the majority of their time on admin rather than analysis.

Automated workflow

However, with digital intake and claims processing automation examples, this is what happens:

1. Digital FNOL via chatbot, mobile app, or customer portal: policy lookup, contact details, incident info are gathered, and the claim is created in seconds.

2. An automated coverage check uses rules engines to validate the policy, checks] policy limits, and apply deductibles. Queues move automatically.

3. AI processes documentation: images, PDFs, medical forms, or police reports are analyzed using OCR (optical character recognition) and NLP, extracting key data fields automatically.

4. AI-driven adjudication flags simple claims for auto-approval, and routes complex ones. Additionally, decision logic applies thresholds. For example, a minor vandalism claim may be approved instantly, but a serious injury claim involving multiple parties is routed accordingly.

API-based payout triggers electronic disbursement: funds can be released via ACH or virtual cards. No manual check run is required.

An automated core system connects each action in real time. There’s no manual queue for initial review or waiting, especially on routine claims. Accuracy improves because data is entered once and reused. Cycle time drops dramatically, and simple claims can be resolved with zero adjuster intervention. For many cases, this closes the loop hours after FNOL with no phone calls or email-chasing for the customer to do. 

EIS uses its event-driven architecture to coordinate these workflows seamlessly across policy, billing, and customer management systems. Each step in the process is triggered by business events, and each tool or partner in your ecosystem can be notified of status changes immediately, ensuring data alignment and process visibility.

The AI revolution is reshaping claims on four fronts: speed, accuracy, cost, and experience. In fact, a 2024 study found that AI-driven technology resulted in a $11.8 million reduction in paid claim amounts over an eight-month period, demonstrating the effectiveness of automation in claims processing.

• Faster settlements: Image-recognition tools can assess help damage in seconds, potentially reducing the need for in-person damage reviews or even human photo review.

   

• Fewer errors: Clean data transfer from one step to another significantly cuts data entry errors.

   

• Lower costs: Automated adjudication gets the adjudication process started, and can help insurers significantly lower cost per claim by reclaiming funds from responsible parties.

   

• Better experience: Customers get real-time updates, digital payments, and less back-and-forth communications.

   

AI can also introduce consistency. Claims are no longer handled based on tribal knowledge or individual interpretation. Instead, models apply standardized logic, evaluate outcomes, and learn from feedback over time. This drives accuracy and makes processes more transparent for regulators and stakeholders.

Key use cases

• Image recognition for damage: AI analyzes photos to classify severity, compare against previous cases, and estimate repair costs.

   

• NLP for documentation: Unstructured data in documents like medical reports, police abstracts, or receipts are parsed instantly within their context to extract relevant fields and data for decisioning.

   

• AI triage tools: Severity and fraud indicators are identified as soon as a claim data enters the system. Low-risk cases can be flagged for auto-resolution, while high-risk ones are escalated to human review.

   

In fact, AWS predicts that by 2030, only 30% of P&C claims will need human intervention, and the other 70% will fall have low or zero human touch involved, thanks to AI and machine learning capabilities driving insurers towards what they call “zero touch claims.” 

An AI agent is a software tool that acts autonomously to support or make decisions in the claims process.

There are two primary agent types:

• Decision-support agents: These agents provide recommendations to human adjusters. They may score fraud risk, suggest payout ranges, or flag missing documentation. The human remains in the loop, and these agents act as assistants to their work, helping them get more done in a day.

• Autonomous agents: These agents act independently, auto-settling low-risk claims or routing cases without any human intervention. They execute within insurer-defined guardrails, but without human input.

Boundaries matter

• AI agents escalate claims to humans when they exceed thresholds or anomalies arise.

• Adjusters retain control over high-risk or litigated claims.

• Every decision is logged, traceable, and tied to business rules for transparency.

This is the foundation of claims processing using AI, not as hype, but as responsible, controlled automation.

Let’s look at some real-world claims automation processing examples that show the transformative impact the technology can have for insurers:

Claims Automation Case Study: Tokio Marine & Nichido Fire

Inefficient claims processes that lack automation can slow down claims approvals, drive up call center costs, and frustrate customers. 

Tokio Marine & Nichido Fire, a leading Japanese P&C insurer, knew this firsthand and wanted to make a major change. Adopting EIS ClaimSmart allowed the insurer to automate their FNOL process and enable customers to submit claims digitally 24/7. The results speak for themselves:

• • Millions saved each year through effective automation and less need for manual intervention.
  • 20% reduction in call center volume, as customers could track claims in real time.

Reducing costs while boosting customer experiences

Without automated claims processing, insurers often can’t process claims digitally and lack reliable, efficient data collection. The burden this puts on agents (due to the frequent need for multiple follow-ups with policyholders) slows down claims and makes errors more likely. This can raise costs, including loss-adjustment expenses. 

All the while, customers can’t stand how slow everything is.

A mutual insurance company serving 15 US states tackled these issues by adopting claims processing automation via EIS ClaimSmart. The solution’s ClaimPulse module allowed them to enable digital FNOL, streamline claims assignments, and equip claims workflows with AI-driven decision-making. Customers could also now monitor and manage their claims digitally.

This led to critical bottom-line benefits:

• Lower LAE, based on the elimination of redundant manual steps.
• Faster claims processing, leading to higher customer satisfaction.

Minimizing claims leakage

Whether due to inefficiency, fraud, or manual error, claims leakage accounts for anywhere from 20% to 30% of all claim payouts. This causes the insurance industry to lose $30 billion each year.

Outdated claims processing systems with little or no automation contribute to the danger of claims leakage. Rules-based fraud detection systems can completely miss new types of fraud that their engineers didn’t know about, or create false positives that waste fraud investigators’ time. 

Meanwhile, these old systems also contribute to subrogation failures and salvage losses. Respectively, carriers can miss chances to recover costs from at-fault third parties and rack up unnecessary costs by not assessing damaged property value fast enough.

Automation-driven EIS solutions including ClaimSmart and ClaimCore can help insurers turn leakage around. For example, they enabled Tokio Marine to use ML-powered fraud detection from ClaimGuard and identify new fraud tactics before they escalate. The insurer enjoyed the following benefits:

• A 5x increase in fraudulent claim detection (and a major drop in false positives).
• A 40% reduction in fraud-related costs.

Meanwhile, the event-driven architecture of ClaimCore® battles other types of leakage. It can automatically detect subrogation-worthy claims, ensuring recovery actions are triggered early to maximize financial recoupment. Automated workflows can also ensure totaled property (like crashed cars) goes directly to salvage, so insurers don’t lose money while the car sits in a repair lot.

While P&C and life insurance now lead many innovation headlines, health insurance was a proving ground for intelligent automation. The complexity, volume, and regulation in health forced the early adoption of AI tools that are now migrating across the industry.

Key components include:

• Prior authorization automation using NLP: patient eligibility and treatment justification parsed instantly from physician notes.

• Claim coding validation via real-time audit bots: fraud risk, overbilling, and guideline compliance flagged before payout.

• Prior-claim history retrieval using integrated APIs: past encounters, provider billing trends, and medical history pulled into a single view.

The result is faster reimbursement, cleaner audits, and lower administrative cost.

These capabilities mirror those in process automation software used across insurance lines. The building blocks are consistent:

• Data pipelines for ingestion

• NLP extractors for structure

• Rules engines for decision logic

• Intelligent agents for escalation or automation

Learn more about claims management with EIS