Insurance AI Use Cases: Practical Value and Governance

Insurance AI use cases span claims, finance, fraud, and risk. Learn how governance and human review enable responsible adoption and business value.

For the insurance industry, the conversation around AI is becoming more practical: Where can it create value today, and what governance needs to be in place before organizations scale its use?

That was a central theme of the Crowe webinar “Exploring Practical AI Use Cases in Insurance.” For many organizations, simply providing employees access to AI tools has not translated into consistent business value. The next step is identifying work that is well suited to AI while embedding appropriate human review and establishing the governance needed to use the technology responsibly.

For insurers, the opportunity can begin with familiar processes across claims, finance, fraud, risk management, and internal audit. Many of these tasks require employees to gather information from multiple sources, apply defined criteria, identify exceptions, and document conclusions. AI can support that work by accelerating analyses and producing reviewable outputs while employees retain responsibility for judgment and decision-making.

Insurance AI use cases to create practical value

AI use cases do not need to begin with wholesale process redesign. In many instances, the most practical starting points are discrete activities that are time-intensive, repetitive, or dependent on reviewing large amounts of information.

Claims payment reconciliation

Finance teams can use AI to compare claims payment activity with general ledger records, identify differences, categorize exceptions, and prepare documentation for review. AI also can help trace reconciling items back to source information and organize results into a consistent month-end package.

A well-designed process can redirect employee attention from manually working through every transaction toward investigating exceptions, validating conclusions, and resolving unusual items. That shift illustrates an important principle for other insurance applications: AI often is most useful when it handles the first pass through a large volume of information and leaves employees to focus on the areas that require judgment.

Claims handling and control analysis

Claims organizations also can use AI to compare process documentation with established control requirements. For example, AI can review a claims process narrative against a defined control framework, assess individual control objectives, identify potential gaps, and organize findings for management or internal audit review. It also can flag areas in which a conclusion depends on an assumption or requires additional judgment.

This type of use case can be particularly helpful because claims processes often span multiple policies, procedures, systems, and roles. AI can bring those sources together and create an initial analysis more quickly.

Human review remains important. A control that appears absent based on AI-produced documentation might operate elsewhere in the process, and an apparent weakness might require additional context before it can be characterized appropriately. The AI output supports the review but it does not replace it.

Journal entry testing

Finance and internal audit functions can use AI to expand journal entry testing. Rather than selecting a limited population for initial review, AI can apply defined testing criteria across a larger set of entries, identify transactions that meet specified risk indicators, summarize the results, and prepare supporting documentation for selected exceptions.

For an insurer, this can increase the amount of information considered during the initial analyses and give employees a more targeted population to investigate.

The quality of the result, however, depends on the criteria being applied. Management and internal audit teams still need to determine which indicators are appropriate, how exceptions should be evaluated, and what supporting evidence is required.

Fraud detection and special investigation unit support

Fraud detection is another area in which AI can help insurers analyze a broader set of information. A special investigation unit can apply established fraud standards, indicators, tiers, and scoring criteria to claims data and use AI to identify potentially higher-risk claims. AI can also reveal patterns that span multiple claims, providers, entities, or other data points that might be difficult to identify through manual review alone.

The resulting output can help investigators prioritize their attention and determine where additional review might be warranted.

The role of AI is to help identify relationships, exceptions, and patterns that deserve investigation. Experienced personnel still need to assess the facts and determine what action, if any, is appropriate.

Financial statement and management analyses

AI also can support recurring financial analyses. An insurer might use AI to review multiple years of financial statements, calculate key ratios, compare results across periods or lines of business, identify trends, and generate draft management-level observations. The technology also can help transform the underlying analyses into tables, charts, or presentation materials for additional review.

This use case can create value in two ways. First, it can reduce some of the manual effort involved in gathering and calculating information. Second, it can make certain types of analyses easier to perform more frequently. A finance team that primarily focuses on completing the close, for example, might be able to devote more attention to recurring trend analyses when AI performs some of the preliminary work.

As with other insurance AI use cases, management should validate calculations, assumptions, and interpretations before relying on the output.

Use business criteria to evaluate AI opportunities

The number of possible AI applications can quickly exceed an organization’s ability to pursue them. Insurance companies therefore need a disciplined way to determine which opportunities are worth advancing.

Leaders can answer the following practical questions to evaluate insurance AI use cases:

• Business impact. Does the use case address a meaningful operational, financial, risk, or customer need?
• People and process readiness. Can employees incorporate the technology into the existing workflow, and is responsibility for review clear?
• Technical feasibility. Can available AI tools perform the task with an appropriate degree of consistency and transparency?
• Data readiness. Is the required data available and sufficiently reliable for the intended use?

These questions help distinguish a compelling AI experiment from a use case that is suitable for operational adoption.

Data readiness is particularly important. Insurers do not necessarily need perfect data before beginning to evaluate AI, but they do need to understand what information a use case depends on, where that information originates, and what limitations might affect the results.

Build governance into AI use from the beginning

Practical value and responsible use need to develop together. AI can process large volumes of information and generate polished outputs quickly. Those capabilities are useful, but they also can create risk if an inaccurate or unsupported conclusion is accepted without sufficient review.

Governance should be part of the AI use case rather than a separate exercise that begins after adoption. Governance and compliance, executive accountability, data use, security, and regulatory alignment are all foundational considerations for scaling AI.

For each use case, insurers can consider several questions:

• Who is accountable for the output? Employees should understand what the AI tool is being asked to do and what the employees remain responsible for reviewing or approving.
• How will outputs be validated? Organizations can define when source information needs to be checked, how exceptions are investigated, and when additional review is required.
• What information can the AI tool access? Privacy, confidentiality, and security controls should reflect the sensitivity of the data involved. Enterprise access controls, data handling requirements, retention practices, and vendor terms all can affect how an AI tool is used.
• How will changes be controlled? A change in model, prompt, data source, or workflow can affect results. Organizations should determine when changes require testing, approval, or updated documentation.
• Can the work be traced? Source references, formulas, assumptions, criteria, and exception reporting can make AI-supported work easier to review.

The level of governance does not need to be identical for every application. A tool that summarizes internal meeting notes presents a level of risk different from one that supports claims decisions, fraud referrals, or financial reporting. Governance can be calibrated to the significance of the AI use case.

From experimentation to repeatable value

The practical opportunity for insurers is to identify specific work that AI can support, redesign that work thoughtfully, and maintain clear human responsibility for the result.

Activities that insurers already perform, such as claims reconciliation, control analyses, journal entry testing, fraud detection, and financial statement analyses, offer a starting point for insurance AI use cases. In each case, AI can help process information, identify exceptions, and prepare a first analysis so employees can spend more time reviewing matters that require judgment.

A combination of practical use cases, disciplined selection, and proportionate governance can help insurers move beyond experimentation and toward repeatable business value.