AI in claims management is changing how organizations think about automation, efficiency and the role technology should play in the claims process.

Nearly every technology discussion now includes promises of AI-powered automation, intelligent claims handling and increasingly autonomous decision-making. Some of those developments are genuinely exciting. AI has the potential to reduce administrative work, make large volumes of information easier to manage and give claims professionals better access to the information they need.

But claims management is not an ordinary automation problem.

Claims involve financial decisions, medical information, personally identifiable information, litigation, regulatory requirements and decisions that directly affect people and organizations. In that environment, the question should not simply be:

How much can AI automate?

A better question is:

Where can AI create meaningful efficiency without compromising the security, accountability and integrity of the claims process?

That distinction may prove to be one of the most important technology decisions claims organizations make over the next several years.

Claims Automation and AI Are Not the Same Thing

One of the problems with the current AI conversation is that very different technologies are often grouped together.

Claims organizations have relied on automation for decades.

Configurable workflows can assign tasks, create diaries, initiate approvals, generate alerts, validate information and route claims according to predefined business rules. These systems are deterministic. The organization establishes the rules, the system follows them, and the resulting activity can be tested and audited.

Generative AI operates differently.

Large language models generate responses based on statistical relationships within data. That makes them remarkably useful for tasks involving language and large amounts of unstructured information. It also means their output is probabilistic rather than inherently deterministic.

NIST identifies “confabulation,” commonly called hallucination, as one of the risks associated with generative AI. A model can produce information that appears authoritative while being incomplete, inconsistent or simply incorrect.

In many applications, that risk can be managed.

In claims, however, the consequences deserve considerably more attention.

An inaccurate summary is inconvenient. An inaccurate conclusion that influences coverage, reserves, medical information, litigation strategy or a payment decision can become a much larger problem.

The distinction is important.

AI can assist a claims professional without becoming the claims professional.

AI Governance in Claims Management Matters

For TPAs, public entities, risk pools, self-insured organizations and insurance carriers, the value of an AI capability cannot be evaluated by speed alone.

Organizations also need to understand what happens behind the interface.

Where does the data go?

Is information retained after processing?

Can it be used to train or improve a model?

Who has access to it?

Can the organization reconstruct how information moved through the process?

What happens when the AI gets something wrong?

Who reviews the result before it affects the claim?

These questions become especially important because claim files may contain personally identifiable information, protected health information, financial information, attorney communications and other highly sensitive records.

An AI tool may save an adjuster several minutes. That efficiency means very little if the organization loses control over sensitive claim information in the process.

For that reason, data governance should be part of the AI conversation from the beginning, not something added after implementation.

AI in Claims Management Is Drawing Regulatory Attention

Insurance regulators are also paying closer attention to how artificial intelligence is being used.

The NAIC Model Bulletin on the Use of Artificial Intelligence Systems by Insurers makes an important point: using AI does not remove an insurer’s existing regulatory responsibilities.

Decisions or actions made or supported by AI systems still need to comply with applicable insurance laws and regulations, including requirements concerning unfair trade practices and unfair discrimination.

The bulletin also emphasizes governance.

Regulators may expect insurers to demonstrate how AI systems are managed, tested, documented and monitored, including systems developed by third parties. Data quality, accountability, model oversight and the potential for adverse consumer outcomes are all part of that discussion.

That changes the technology question considerably.

It is no longer enough for a vendor to say, “Our system uses AI.”

Claims organizations need to be able to ask:

What does the AI do, what data can it access, what controls surround it, and can we defend the process if someone asks us to explain it?

The Black-Box Problem

Imagine an AI system recommends escalating a fraud investigation, changing a reserve or taking an action that ultimately affects a claimant.

Why?

A traditional business rule may be relatively easy to reconstruct:

A defined condition was met. A configured rule was triggered. The system created a specific action.

With a complex AI model, the explanation may be much less straightforward.

That becomes problematic when organizations need a defensible record of what happened and why.

Claims systems are systems of record. Auditability is not an optional feature.

This does not mean advanced analytics or AI should never identify patterns or surface potential issues. It means organizations should be cautious about allowing an opaque model to become the final authority for consequential claim decisions.

AI may identify something worth reviewing.

The claims professional should still have the ability to understand the underlying information, evaluate the recommendation and make the appropriate decision.

Practical Uses of AI in Claims Management

The most useful applications of AI may ultimately be less dramatic than “autonomous adjusting.”

They may also deliver far more practical value.

Consider the amount of unstructured information that enters a claims operation every day: medical records, invoices, correspondence, reports, attachments and first notice documents.

AI and intelligent document processing can help classify those documents, identify relevant information, organize incoming data and prepare it for review.

Instead of asking AI to determine coverage, the system could help extract the information an adjuster needs to evaluate coverage.

Instead of allowing AI to make a fraud determination, technology could surface duplicate invoices, unusual timelines or other defined indicators for investigation.

Instead of allowing a model to independently alter the official claim record, extracted information could be presented to an authorized user for verification before it becomes part of that record.

That is still meaningful automation.

It simply puts the technology in the right role.

Protecting the Claims System of Record

A responsible architecture creates boundaries between experimental or probabilistic technologies and the core claims environment.

Rather than giving an AI model unrestricted access to a claims database, organizations can limit what information reaches the model, establish appropriate security and retention controls, validate outputs and require approval before information is committed to the system of record.

The exact architecture will vary by organization and use case, but the principle is straightforward:

AI should operate within the organization’s security and governance framework. The security and governance framework should not be redesigned around the AI.

That approach allows organizations to explore new capabilities without abandoning the controls that make enterprise claims systems dependable.

Claims Automation Is Already Powerful

There is also a tendency to underestimate what established workflow automation can accomplish.

A modern claims platform does not need to independently “think” like an adjuster to eliminate substantial amounts of administrative work.

FileHandler Enterprise™, JW Software’s claims management platform, uses configurable workflows and business rules to help organizations automate routine processes throughout the claim lifecycle.

Tasks can be assigned automatically. Reports can be scheduled and distributed. Rules and validations can help standardize processes. Alerts and workflows can help prevent important activities from falling through the cracks.

The objective is not to remove the professional from the process.

It is to remove unnecessary work around the professional.

That distinction matters.

When technology handles repetitive administrative activity consistently, claims professionals have more time for the work that actually requires their experience: investigation, communication, evaluation, negotiation and judgment.

Security May Be the More Important Innovation

The insurance technology market is understandably excited about artificial intelligence.

JW Software is too.

But innovation should not be measured by how quickly a company can attach AI to every part of its platform.

For organizations responsible for sensitive claims information, innovation also means knowing when technology should automate, when it should assist and when a human being should remain firmly in control.

It means protecting the system of record.

It means maintaining an auditable process.

It means understanding where sensitive data travels and who can access it.

And it means building technology on a security foundation capable of supporting the responsibilities claims organizations already carry.

FileHandler Enterprise is built around that foundation, with configurable claims workflows, detailed data tracking, access controls, reporting and an enterprise hosting environment supported by security and compliance standards including SOC 1, SOC 2, SOC 2 + HITRUST, HIPAA, GLBA, PCI-DSS and NIST.

The future of claims technology will undoubtedly include more artificial intelligence.

The organizations that succeed will not necessarily be the ones that automate the most.

They will be the ones that know what to automate, what to protect and where human judgment still matters.

About JW Software

Since 1989, JW Software has developed technology for organizations managing complex claims and risk operations. FileHandler Enterprise™ is a configurable claims management platform supporting TPAs, insurance carriers, public entities, self-insured organizations and other claims professionals with workflow automation, reporting, integrations and secure claims data management.

To learn more about FileHandler Enterprise and how JW Software approaches secure, configurable claims management, visit JW Software or request a demonstration.

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