Quick Summary

Claims processing automation removes the manual re-keying, system-switching, and routing that slow down claims before they even reach an adjudication engine. To do it the right way:

• Unify your intake across EDI, paper, fax, email, and portals.
• Separate clean claims from those that need review.
• Use the right technologies for each step.
• Build around your existing systems instead of replacing them.
• Keep humans in the loop for exceptions and complex decisions.

DataBank can help you easily put these steps into practice, from capturing and processing incoming claims documents to validating the data and routing it into your existing systems.

Processing Claims Manually Can Incur Massive Costs

Before even a single health plan claim can be manually processed and paid, your claims team has to reconcile information across eligibility data, medical codes, supporting documentation, and payer-specific rules.

Multiply that across the thousands of claims you receive each day, and the administrative workload adds up fast. And so does the cost of the staff time required to keep those claims moving.

In fact, the 2024 CAQH Index found that U.S. healthcare still spends roughly $90 billion a year on routine administrative transactions. An estimated $20 billion in savings is also available just by switching from manual workflows to electronic ones.

And that’s only the financial cost.

Operationally, manual claims processing involves handoffs between multiple people and teams. Each handoff creates another opportunity for errors to compound, denials to increase, and processing times to stretch. And when claims processing slows down, providers wait longer to get paid, and members wait longer for answers.

The obvious fix is to automate your claims processing. But that doesn’t mean ripping out your core administration system and plugging in another piece of software. It means building automation around the system you already use, so routine work moves faster while your claims professionals stay in the loop where human judgment is needed. This article teaches you how to do exactly that.

Why Listen to Us?

DataBank has processed claims-related documents for healthcare payers for over 30 years. For one national healthcare payor division, we processed 3.5 million images in one month across 16 payer clients, with 100% SLA adherence on fraud and claims integrity communications.

Our experience combining AI-powered processing with human validation for claims processing automation is what informs this article.

What Is Claims Processing Automation?

Claims processing automation uses technology to handle repetitive, rule-based steps in the claims lifecycle so staff can focus on the steps that actually require human judgment.

Once in place, it flows something like this.

A member visits an orthopedic specialist. The provider submits an 837 Professional transaction through a clearinghouse. If the claim is clean, it flows straight into the payer’s adjudication engine and gets matched against the member’s plan. An 835 remittance goes back to the provider. The entire routine processing happens automatically, without a person having to move the claim between systems.

But without automation, multiple people, systems, and handoffs would be involved.

Someone would download the 837 and re-key the procedure codes into a separate system. They’d pull up the member’s eligibility in another screen, check the benefit limits in a third, and flag a question about a modifier. They’d email a colleague, wait for a response, and finally enter the payment once it comes in. That’s three systems, two handoffs, and at least a day of lag for a straightforward claim.

Automation helps you avoid this time waste without replacing clinical judgment.

It removes the re-keying, system-switching, and manual routing that slow claims that never needed a human decision, letting your team focus on exceptions, complex claims, and decisions that require expertise.

Why Automate Claims Processing?

Claims processing automation matters because:

1. Claims Volume Keeps Growing, But Headcount Isn’t

Health plan enrollment has climbed steadily since 2020, and every new member means more claims, more correspondence, and more exceptions. But hiring hasn’t matched that growth. Automation can fill that capacity gap.

2. Manual Handling Creates Inconsistency

Two adjusters reviewing the same claim may reach different conclusions.

But if that process is automated, the same rules are applied the same way every time. This keeps decisions consistent, especially for routine checks like eligibility checks, benefit limit lookups, duplicate validation and coding validation, which shouldn’t need a judgment call in the first place.

3. Automation Reduces Claims Processing Costs

When multiplied across your entire claims volume, manual processing costs add up quickly. Automation can reduce those costs by cutting the time your team spends on each claim. For example, in May 2026, Aetna, a payer with 37 million members, reported that its Claims Assist Manager cut processing time by over 20% on complex claims requiring manual review.

The Common Terminologies Used in Claims Processing Automation

Vendors use a lot of overlapping terminology when they talk about claims processing automation. Here are the main ones you’ll come across and what they mean:

OCR (Optical Character Recognition)

OCR converts text in an image into machine-readable characters. For example, it can take a scanned CMS-1500 and turn the form’s text into digital text. But OCR doesn’t understand the meaning or context of the text it extracts. It can pull out a string of numbers from an image, but can’t tell whether they represent a procedure code, date, or member ID.

OCR works best on clear, typed documents. Handwriting, poor scans, and inconsistent layouts can reduce accuracy.

IDP (Intelligent Document Processing)

IDP goes a step further by turning document content into usable data.

An IDP system doesn’t just read the text. It identifies the document type, locates relevant fields, extracts their values, and validates them against expected formats. For claims processing, that might mean recognizing a CMS-1500, finding the member ID and procedure codes, and passing those values into the next step of the workflow.

Modern IDP platforms use AI and machine learning to handle different document layouts and formats and can achieve a field-extraction accuracy of 95 to 99%.

RPA (Robotic Process Automation)

RPA automates repetitive actions that a person would normally perform across applications. It can log into a legacy portal, check eligibility, copy the result, and enter it into another system.

That makes RPA useful when systems don’t have APIs or other ways to exchange data directly. But RPA follows predefined instructions, so it can struggle when a screen changes or an unexpected situation requires a different action.

Workflow Orchestration

Workflow orchestration is the routing layer. It controls what happens next as a claim moves through the process. It uses rules, data, and claim information to route claims to the appropriate system, queue, or person.

For example, a clean claim might move to adjudication, while one that fails a validation check goes to a review queue. A claim that has been waiting too long can also be escalated.

Orchestration doesn’t process the claim itself or make clinical decisions. It just coordinates the different steps so claims get to the right place at the right time.

Agentic AI

Agentic AI is a newer automation layer, and unlike RPA, which follows scripted steps, agentic AI can reason through a claim. It can gather information from multiple sources, assess findings, and decide what to do next

Aetna’s Claims Assist Manager is an example. It uses AI agents to pull eligibility, coverage, member, and provider data together and recommend next actions.

But while Agentic AI systems can handle exceptions that don’t neatly fit predefined rules, they still need human oversight for clinical and coverage decisions.

N.B.: Most automation setups combine several of these technologies rather than rely on just one. The right combination depends on where the manual work is in your claims process.

How to Identify the Claims Ready for Straight-Through Processing?

The claims best suited for automation are those that can be solved efficiently, even if they move from intake to payment, without a single human reviewing them. Those kinds of claims often meet these 4 criteria:

They arrive electronically, so they can be processed without manual data capture. For example, EDI 837 forms.

They include the correct info across all relevant fields, and so can pass all validation checks on the first submission.

They involve routine services with clear plan rules, so there’s little or no need for a clinician’s interpretation.

They don’t require supporting documentation, so there’s no need to route them for a manual review.

Claims that meet these criteria are relatively straightforward, so having your team spend time reviewing them is a huge time drain.

Instead, automate them and let your adjusters focus on the claims that actually need their attention: the ones that arrive by paper or fax, contain missing or inconsistent information, involve non-routine services, or require supporting medical or operative reports.

The Usual Steps in Claims Processing Automation

Once you decide which claims you want to automate their processing, here’s how to layer your automation workflow step by step.

1. Intake

Your automation workflow should first capture claims from the channels you receive them through. That could be an EDI 837 transaction through a clearinghouse, a paper CMS-1500 form in the mail, a fax, a portal submission, or a mix of all of the above. The information you receive is mostly the same across these channels, just in different formats.

2. Capture and Extraction

Next, your workflow should turn incoming claim information into a structured format your claim systems can use.

EDI transactions are already structured, so they can move through with minimal processing. But paper forms, faxes, and other documents need to be digitized and processed to extract fields like the member ID, date of service, procedure and diagnosis codes, provider NPI, and billed charges.

This is where OCR or IDP can replace much of the manual data entry involved in processing unstructured claims and prevent potential errors from creeping in.

3. Validation Against Eligibility and Coverage

Once the claim data is structured, your workflow should check it against your eligibility and coverage data. It should verify whether the member was covered on the date of service, whether the provider is in network, whether the service required prior authorization, and whether the benefit plan covers the service.

Claims that pass these checks should continue to adjudication. Claims that fail, though, should be routed to a queue for further review.

4. Adjudication

Now comes the actual payment decision. Your system should apply the plan’s payment rules, fee schedules, and coordination-of-benefits logic to determine whether the claim gets paid and at what rate.

Routine claims should usually move through adjudication automatically. But claims with medical-necessity questions or coordination across multiple plans should go to an adjuster to cross-check.

5. Settlement and Correspondence

Finally, your automation system should issue the payment or denial and generate the explanation of benefits for the member. It should also send an 835 remittance advice to the provider showing how the claim was processed.

If a provider appeals a denial, the claim should move into a separate workflow with its own documentation and review requirements.

What Makes Health Plan Claims Automation Different

Healthcare payers face distinct data formats, regulatory requirements, and documentation rules that make their claims automation different. If you’re running health plan claims, here are the specifics that matter:

1. 837 and 835 Transactions Are the Electronic Backbone

The HIPAA-mandated 837 transaction is the standard format for submitting health claims electronically. It comes in Professional, Institutional, and Dental versions. The 835 is the electronic remittance advice sent back to the provider after you process the claim.

Together, these transactions form the backbone of electronic claims exchange. Before an 837 reaches you, the clearinghouse will typically validate it to catch formatting errors, missing fields, and code-set violations.

2. Clean Claims Should Be Handled Differently From Dirty Claims

Industry benchmarks from HFMA and MGMA define a clean claim as one that passes all validation checks on first submission. That means the member ID is correct, the provider NPI is valid, the coding is accurate, and any required authorization is on file. These claims are good candidates for efficient, high-volume automation because there’s little reason to stop them for manual review.

A dirty claim is the opposite: it has an issue that needs to be resolved before it can move forward. That might be an invalid member ID, a procedure code that doesn’t match the diagnosis, a missing referral, or an expired authorization.

Treating clean and dirty claims the same, especially by sending dirty claims straight through the same automated workflow for clean claims, is where you can start running into problems.

Dirty claims should first be flagged and routed to the appropriate team for correction or review. Otherwise, an issue that could have been caught early can move further into the claims process and result in an incorrect payment or denial.

The KFF analysis of 2024 marketplace plans illustrates why this matters. Insurers denied 19% of in-network claims, with reported reasons including administrative errors (25%), excluded services (13%), and missing prior authorization (9%). Some of these issues could have been identified and resolved earlier if dirty claims had been flagged and routed for review before processing.

3. Some Health Claims Require Additional Records That Often Arrive Unstructured

Some health claims require supporting documentation that doesn’t typically arrive with the 837 transaction. These might include operative reports, pathology results, discharge summaries, prior authorization letters, or documentation from another payer for coordination of benefits.

These records often arrive separately by fax, mail, or portal upload. Because they’re usually unstructured, capturing the information, extracting the relevant data, and linking it to the right claim can be one of the more difficult parts of health claims automation.

4. A Denial Results in a Separate Appeal Workflow

When a health claim is denied, the provider or member can appeal. That appeal creates additional documentation requirements, review timelines, and correspondence beyond the original claims workflow.

And the appeal workflow is harder to automate because most appealed cases are complex and require thorough clinician or coverage review.

This creates a situation in your health claims automation whereby the most complex cases end up getting the least technological support.

The Common Challenges in Health Claims Automation

Here are some challenges you might run into as you try to automate your claims processing:

1. Handwritten and Poor Quality Scans

OCR accuracy drops significantly when forms are handwritten or scans are low quality. A CMS-1500 that has been faxed and copied multiple times, for example, may not extract cleanly, and those errors can cascade into downstream processing.

IDP can improve extraction, but the lasting fix is a validation layer that catches errors before they enter the adjudication workflow. That’s where subject matter expert validation comes in. Trained reviewers can check fields that IDP flags as low-confidence before the data moves forward.

You can read more about the practical limits of IDP and where human oversight fits in this DataBank guide.

2. Keeping Multi-Document Claims Well Documented

A single claim can come with several supporting documents: an operative report, lab results, a prior authorization letter, or a coordination-of-benefits explanation. These documents may arrive together, or they may show up days apart through different channels.

Your automation needs to be able to match all these documents into a single claim package and flag missing fields before adjudication. Otherwise, someone would have to track down missing documents and manually piece the claim together.

3. Missing or Invalid Member IDs

When a member ID doesn’t match the eligibility system, everything downstream stops. The claim can’t be validated, can’t be adjudicated, and sits in a queue until someone resolves the mismatch.

And the cause might be as simple as a transposed digit or outdated member information. It could also be a name change or a retroactive enrollment or termination. Automated validation can catch these issues at intake and flag them for correction before they become a bigger problem.

4. Legacy System Integration

Most payers don’t run a single claims system. They run several, often from different eras, with different data models and different interfaces.

Connecting a new automation layer to this kind of legacy core admin system is usually the hardest part of the project. The data mapping, testing, and parallel-run requirements can easily take much longer than estimated.

5. Unowned Exception Queues

Every claims operation has exception queues for claims that failed a rule, need additional documentation, or require a manual decision. But when no one owns those exception queues, claims sit, age, and eventually get worked in batches, often by whoever has capacity rather than whoever is best equipped to resolve them.

If you automate the straightforward claims without fixing your exception handling, you haven’t really removed the bottleneck. You’ve just left the hardest-to-process claims in a queue where they’re more likely to be delayed or mishandled.

The Most Important Step for Successful Claims Processing Automation

When it comes to automating your health claims processing, the success of everything downstream depends on the quality of your intake.

While structured EDI 837 transactions, which are already automation-ready, make up the majority of claims volume for most payers, other formats still come in, too.

Paper claims still arrive, especially from smaller provider offices, out-of-network providers, and member-submitted claims. Fax also remains common for attachments, medical records, and additional correspondence, even when the claim itself arrived electronically. And email and portal submissions are still part of the mix, often combining structured fields with attachments in different formats.

These claim formats range from semi-structured to completely unstructured, so you need to capture, digitize, classify, and convert them into usable data before your automation can act on them.

Without this processing layer, your automation workflows get only some, not all, of the available information, leaving claims incomplete and creating gaps that still require manual intervention.

So, unify your intake across all channels. Bring all structured and unstructured documents into one workflow, so your automation can process complete, connected information from the start.

How DataBank Helps With Automated Claims Processing

DataBank’s work with payers starts with that most important step: quality data intake across all channels. That means high-volume document scanning across paper, fax, email, and portal submissions.

From there, our intelligent document processing classifies and extracts your claim data with built-in human validation. We then build workflow automation that routes everything into your downstream systems.

One national payor division moved its fraud and claims integrity operation to DataBank, and we helped them scan 3.5 million record images in one month from paper, fax, CD, and portal sources. Their documents were then matched and merged with human expert review on edge cases. The result was a 100% SLA adherence across 16 payer clients.

DataBank’s approach runs through three connected layers. Document Intake and Data Capture handles the physical and digital conversion. Content management governs the records over time. And workflow automation connects the pieces so claims move through your operation instead of sitting in disconnected queues.

All of that processing happens in DataBank’s 3 U.S.-based, SOC 2 Type II-certified facilities. If you are a payer handling PHI under HIPAA or an organization with data residency requirements, DataBank can provide the secure processing infrastructure to support your claims workflow.

Ready to take the manual work out of claims intake? Talk to DataBank about how we can help your claims and benefits processing.

Start Your Claims Automation With Quality Intake

Most claims automation conversations start at the wrong end, with the adjudication engine. But your adjudication engine is only as good as the data it receives.

For most payers, the real bottleneck is further upstream. It’s the paper that hasn’t been scanned, the fax that hasn’t been matched, and the portal submission that hasn’t been validated.

If you’re a payer starting out with claims automation, map every channel through which data enters your operations and work forward from there. Unify your intake first, then automate the downstream steps that are repetitive, rule-based, and don’t require manual judgment.

That gives your automation a complete, reliable stream of information to work with, and your team a clearer path to handling the claims that actually need their attention.

Need help identifying where to start? DataBank’s team can assess your current claims processing operation and help you identify the operational challenges that are good candidates for automation.

Reach out today to see what that could look like for your organization.

FAQs

1. What’s the Difference Between Claims Automation and Auto-Adjudication?

Claims automation covers the broader claims lifecycle, from intake and data capture through validation, routing, adjudication, and settlement. Auto-adjudication is just one part of that process. It uses predefined rules to determine how a clean claim should be processed and paid without manual review.

You can automate your adjudication process, but if the data entering the engine is incomplete or incorrect because intake is still manual, the output will be wrong too.

2. How Much Does it Cost to Process a Claim Manually Versus Electronically?

Costs vary by organization, but the CAQH Index consistently shows a substantial gap. The industry collectively spends roughly $90 billion a year on routine administrative transactions, with an estimated $20 billion in savings available from further automation. The specific cost difference per claim depends on your volume, your payer mix, and how many channels your claims arrive through.

3. What Percentage of Claims Can Be Auto-Adjudicated?

It depends entirely on your claim mix and your clean claim rate. Organizations with clean claim rates above 95% can auto-adjudicate a large share of routine claims. But claims requiring medical necessity review or coordination of benefits across multiple payers will still need human involvement. The goal isn’t to automate every claim. It’s to automate as much of the routine volume as possible so your team can focus on the claims that actually need their expertise.

4. Can Automation Reduce Claim Denials?

Yes, by catching the errors that cause denials earlier in the process. That means validating member eligibility at intake, checking coding accuracy before submission, and flagging missing authorizations before the claim reaches adjudication. The Experian 2025 State of Claims survey found that 69% of organizations using AI for claims reported reduced denials or more successful resubmissions.

5. Do I Need to Replace My Core Admin System to Automate Claims?

No. Most claims automation sits alongside your existing systems rather than replacing them. IDP handles document intake, RPA bridges legacy systems that lack APIs, and orchestration layers route work between systems.

The end result is clean, structured data flowing into the systems you already have. You don’t need to rip out your core admin system and start over.