How AI Can Help Insurance Companies: 7 Practical Use Cases

Insurance companies have invested heavily in digital systems over the past two decades. Yet many everyday processes still rely on people collecting information from emails, PDFs, policy systems, spreadsheets, and customer portals before they can make a decision.

An underwriter reviews submission documents before assessing risk. A claims specialist pieces together information from multiple sources to understand what happened. A broker searches across systems to answer what should be a simple customer question.

Artificial intelligence can reduce much of this manual work.

Rather than replacing insurance professionals, AI can organise information, identify missing details, summarise documents, and recommend the next step. This allows underwriters, claims teams, and customer service staff to spend more time making decisions instead of gathering data.

The greatest value comes from improving the workflow around insurance decisions, not removing people from the process. Complex claims, underwriting decisions, customer advice, and regulatory responsibilities still require human judgement and accountability. Organisations looking to redesign these workflows often start with custom insurance software development services that integrate underwriting, claims, policy administration, and AI into a single platform. 

In this guide, we'll look at where AI delivers the greatest operational value, the workflows where it makes the biggest difference, and how insurers can introduce it safely.

How can AI help insurance companies?

AI helps insurance companies automate repetitive, document-heavy work such as extracting information, preparing underwriting submissions, triaging claims, detecting potential fraud, and supporting customer service. It improves how information moves through insurance workflows while leaving important decisions to experienced professionals.

AI in insurance at a glance

AI adoption in insurance has moved well beyond experimentation.

Research cited in EY's 2025 Global Insurance Outlook suggests that GenAI-enabled automation could increase underwriting capacity by 35%, allowing underwriters to process more submissions without proportionally increasing administrative work.

A 2025 survey from the National Association of Insurance Commissioners (NAIC) found that 84% of responding health insurers already use artificial intelligence or machine learning somewhere in their operations.

Today, insurers apply AI across a wide range of activities, including:

• Underwriting support

• Claims processing

• Fraud detection

• Customer service

• Pricing

• Risk adjustment

• Document processing

These figures don't mean every insurer should automate every process. They do show that AI is becoming another operational tool, much like workflow automation or cloud platforms before it. The organisations seeing the greatest value usually begin with one clearly defined business process rather than trying to automate everything at once.

Why insurers are investing in AI

Insurance has always been a data-intensive industry. Every policy, claim, endorsement, renewal, inspection report, and customer interaction creates more information that needs to be reviewed before someone can take action.

The challenge is that much of this information remains unstructured.

Submission packs arrive as emails with multiple attachments. Claims include photographs, invoices, repair reports, and correspondence. Underwriters often need to compare information across several systems before assessing a single risk.

At the same time, insurers face growing pressure to improve customer response times while managing operational costs. Customers expect faster quotes and quicker claims updates. Brokers want faster underwriting decisions. Internal teams want fewer manual tasks and less time spent searching for information.

AI addresses these operational challenges by helping employees work with information more efficiently.

Instead of asking an underwriter to read a 60-page submission manually, AI can organise the documents, identify missing information, compare details with underwriting guidelines, and prepare a structured summary for review.

The underwriter still makes the decision. The difference is that they spend their time assessing the risk rather than organising the paperwork.

This workflow-first approach often creates more value than simply introducing an AI chatbot or adding another standalone tool. When AI supports the process where work actually happens, the benefits become much easier to measure.

7 practical ways insurers can use AI

1. Process insurance documents faster

Insurance operations generate large volumes of documents every day. Policy applications, claims forms, medical reports, inspection documents, emails, contracts, invoices, and supporting evidence all need to be reviewed before work can continue.

Much of this effort involves finding information rather than making decisions.

AI can automatically extract important details from these documents, organise them into structured data, identify missing information, and prepare summaries for employees. Instead of opening multiple files, an underwriter or claims specialist can begin with a clear overview of the case.

This becomes particularly valuable when information needs to flow between underwriting, claims, policy administration, and customer service systems.

Human review remains essential whenever information is incomplete, inconsistent, or likely to affect an important insurance decision.

2. Prepare underwriting submissions

Underwriting is rarely slowed down by the final decision itself. More often, delays occur because underwriters need to gather information before they can begin assessing the risk.

Broker submissions may include dozens of documents, some complete and some missing important details. Underwriters often compare information against internal guidelines while checking previous policies, customer history, and supporting reports.

AI can reduce much of this preparation work.

It can organise submissions, identify missing documents, compare information against underwriting rules, and prepare a structured summary for review. The result is a cleaner starting point that allows underwriters to focus on evaluating the risk rather than collecting information.

Importantly, AI supports the underwriting process. The final decision remains the responsibility of the underwriter. As insurers move beyond document summarisation, many are adopting agentic AI to automate multiple steps within the underwriting workflow.

3. Triage claims more efficiently

Claims handling often involves reviewing several sources of information before the right team can begin work.

A single claim may include customer statements, photographs, repair estimates, invoices, assessor reports, emails, and historical policy information. Bringing all of this together manually takes time and can delay straightforward claims.

AI can classify incoming claims, extract key information from supporting documents, identify missing evidence, and recommend the most appropriate workflow based on predefined business rules.

For example, a low-value motor claim with complete documentation may be routed directly to a standard claims team, while more complex cases involving injuries or disputed liability can be escalated for specialist review.

This allows claims teams to prioritise their work more effectively without removing human oversight from important decisions.

4. Detect potential fraud earlier

Insurance fraud rarely appears as one obvious indicator.

Instead, it often emerges through patterns that become visible only when multiple pieces of information are considered together. Similar addresses, repeated phone numbers, unusual claim timing, modified documents, or connections between different policies may all indicate that a case deserves closer attention.

Machine learning models are well suited to identifying these kinds of relationships across large datasets. They can highlight unusual behaviour that would be difficult for employees to detect through manual review alone.

However, identifying a suspicious pattern is not the same as proving fraud.

AI should provide investigators with additional evidence and prioritise cases for review. Decisions involving fraud require careful investigation, supporting evidence, and appropriate governance before any action is taken.

5. Support customers and brokers

Insurance customers want quick answers. Brokers want faster responses from underwriting teams. In many cases, neither question is particularly complex. The delay comes from finding the right information across multiple systems.

AI can retrieve policy details, explain standard procedures, identify outstanding documents, and prepare responses for customer service teams. It can also help brokers check the status of a submission or understand the next step in the underwriting process.

For example, a broker asking whether additional documents are required shouldn't need an underwriter to manually review every attachment. AI can compare the submission against product requirements and identify what's missing before the underwriter begins their assessment.

This creates a better experience for customers and brokers while reducing interruptions for operational teams.

Questions involving policy interpretation, complaints, complex claims, or coverage advice should still be handled by experienced employees.

6. Simplify policy administration

Policy administration involves far more than issuing new policies. Teams manage renewals, endorsements, cancellations, product updates, and policy amendments, often across several internal systems.

Many of these activities follow predictable business rules but still require employees to move information between applications, compare policy versions, or confirm that required information is complete.

AI can support these tasks by identifying changes between policy versions, preparing renewal summaries, highlighting missing information, and guiding employees through internal procedures.

The greatest benefit usually comes when AI connects directly with policy administration platforms, document repositories, CRM systems, and customer portals through secure APIs.

Rather than replacing existing systems, AI helps people use those systems more efficiently.

7. Modernise legacy insurance systems

Many insurers rely on software that has evolved over years, sometimes decades. Product rules, integrations, custom workflows, and batch processes have often been added gradually as the business has grown.

Modernising these platforms can be difficult because the technical documentation no longer reflects how the system actually works.

AI can assist engineering teams by analysing existing code, documenting business rules, identifying dependencies, generating test cases, and explaining how different parts of the platform interact.

This can significantly reduce the discovery work required before a modernisation project begins.

However, legacy systems usually contain product-specific exceptions, regulatory requirements, and operational knowledge that exists only within the business. Technical expertise and insurance domain knowledge remain essential throughout the project.

Where AI fits across insurance workflows

The most successful insurance AI projects improve existing workflows rather than introducing automation for its own sake.

Across each workflow, AI reduces administrative effort. Employees continue to make decisions that require experience, judgement, regulatory understanding, and customer communication.

How should insurers start using AI?

The biggest mistake organisations make is starting with the technology instead of the operational problem.

Many insurers begin by exploring AI models or experimenting with chatbots before deciding which workflow they actually want to improve. This often creates isolated pilots that deliver little long-term value.

A better approach is to begin with one process that is repetitive, measurable, and heavily dependent on documents or manual information gathering.

A practical roadmap looks like this:

1. Identify one workflow that creates operational delays.

2. Measure the current process, including time, manual effort, and common errors.

3. Map the systems, documents, and decision points involved.

4. Identify where AI can support employees rather than replace them.

5. Define which decisions always require human approval.

6. Test the solution using real business cases before expanding to other workflows.

For many insurers, broker submission intake, document classification, or claims triage provide a practical starting point. These workflows involve high volumes of repetitive work while allowing people to remain responsible for important decisions.

As confidence grows, AI can be introduced into additional parts of the insurance operation.

AI works best when it's built into the workflow

AI has the greatest impact when it becomes part of the systems employees already use.

During our work on Numerisk's Granite platform, we saw how important the underlying architecture is before introducing more advanced automation. The platform combined an API-driven architecture, event-based processing, secure authentication, automated deployment pipelines, and separate development, testing, and production environments.

Although Granite was not built as an AI platform, this type of architecture creates a much stronger foundation for future AI capabilities. Data is easier to access, permissions are clearly defined, and important actions can be audited across the workflow.

In practice, successful AI projects depend just as much on architecture, integrations, security, and business processes as they do on the AI model itself. This is why many insurers combine workflow modernisation with specialised AI development services to build secure, production-ready AI solutions that integrate with existing insurance systems.

Conclusion

AI can help insurance companies reduce manual administration across underwriting, claims, fraud detection, policy administration, and customer service. The biggest gains usually come from helping employees work with information more efficiently rather than automating entire jobs.

The best place to begin is a single workflow where delays, document handling, and repetitive tasks can be measured. Once that process is working reliably, insurers can expand AI into other parts of the business with greater confidence.

At Whitefox.cloud, we help insurers, MGAs, brokers, and insurtech companies design and build custom insurance platforms that support underwriting, claims, policy administration, distribution, and AI-enabled workflows. Explore our insurance software development Services to see how we approach architecture, integrations, and workflow automation.