AI Use Cases in Insurance: From Claims Automation to Fraud Detection

Introduction

Insurance fraud costs the United States an estimated $308.6 billion every year, according to a third-party report from the Coalition Against Insurance Fraud, covered by Reinsurance News. That cost shows up in every policyholder's premium, and it lands on the same teams that already handle a heavy load of claims, billing, and coverage calls.

Insurers are turning to AI to carry some of that load. The best AI applications in insurance are not science projects. They are practical tools that answer calls, check identities, score every conversation, flag risky patterns, and surface what customers keep asking about.

This guide walks through 11 AI use cases in insurance, with a plain explanation of how each one works and where it pays off. You will also see how one insurance provider used Level AI to improve scoring accuracy, cut handle time, and raise first call resolution.

What Is AI in Insurance?

AI in insurance is software that can understand language, spot patterns in data, and take action on tasks that used to need a person. In practice, that means software that listens to a phone call, reads a claim file, or reviews a transcript and then acts on it, such as answering a question, scoring an agent, or raising a flag.

Two kinds of AI show up most often in insurance:

• Predictive AI looks at past data to estimate what will happen next, such as how likely a claim is to be fraudulent or how risky an applicant is.

• Generative AI understands and produces natural language. It powers AI voice agents that hold a real conversation, tools that summarize a call, and systems that score a conversation against a checklist. Insurance use cases for generative AI are growing fastest in the insurance contact center, because so much insurance work happens through conversations.

Both kinds work best when they feed each other. A voice agent handles the call, a scoring system checks the call, and an analytics layer finds the pattern across thousands of calls.

What Are the Top AI Use Cases in Insurance?

The 11 use cases below cover the full policy journey, from the first question to underwriting. The table gives a quick view, and the sections after it explain each one.

1. Policy Inquiries and Document Requests

Policyholders call to ask what their plan covers, what their deductible is, or where to find a proof of insurance card. These are simple questions, but they fill queues and take agents away from harder work.

AI voice agents (software that talks with callers in natural speech and completes tasks for them) can answer these questions at any hour, pull the right policy details from your systems, and send documents by text or email while the caller is still on the line. This follows the same approach used to automate tier 1 support, where repeat questions are handled by AI and people take the rest.

2. Claims Assistance, Automation, and Status Updates

Claims are the moment of truth for an insurer. A customer who just had an accident wants a fast answer, not a hold queue. AI for insurance claims covers three parts of that experience:

• First notice of loss (FNOL). This is the first report that something happened. An AI agent can collect the date, location, people involved, and a short description, then write that data into the claims system in clean fields.

• Document collection. AI asks for missing photos, repair estimates, or police reports and sends reminders until they arrive.

• Status updates. "Where is my claim?" is one of the most common calls an insurer gets. AI can check the claim record and give an accurate answer in seconds.

Adjusters stay in charge of coverage decisions and large losses. AI takes the repeatable steps around them. For a deeper look at how this works across the claims process, see this list of the best AI voice agents for enterprise insurance.

3. Customer Verification Before Account Access

Before an agent shares a claim balance or changes a beneficiary, someone has to confirm the caller is who they say they are. Manual checks are slow, and an agent in a hurry can skip a step.

AI can run the verification flow the same way on every call. It asks the required questions, compares answers with the policy record, and only then opens access to account details. It can also keep sensitive details out of stored records. PII redaction (removing personal data such as card numbers and birth dates from transcripts and recordings) protects customers and reduces the data you have to secure.

4. Payment Queries and Automated Premium Reminders

Billing calls are predictable. Customers ask when a payment is due, whether a payment went through, or how to change a payment method. Late payments are just as predictable, and a missed premium can lead to a lapsed policy.

AI voice agents can answer billing questions and reach out before a due date with a friendly reminder. They can take a payment through your secure payment system or send a link. Outbound calling by AI agents lets a small team contact thousands of policyholders without adding headcount, which helps keep policies active.

5. Support Automation and Intelligent Human Transfers

Automation does not mean every call stays with AI. The goal is to resolve simple requests fully and send the rest to the right person with no loss of detail.

A good handoff matters as much as the automation itself. If a caller has to repeat their story after a transfer, trust drops fast. Level AI's context handoff passes the conversation summary to the human agent so the customer does not start over. For sales and quote calls, AI can also qualify the caller first and use live transfer for insurance to connect them to a licensed agent right away.

6. Automated Quality Assurance for Insurance Calls

Traditional quality assurance means a reviewer listens to a few calls per agent and fills out a scorecard. A small sample can miss the calls that matter most.

AI reads and scores every conversation against your existing scorecard. It checks things like whether the agent greeted the customer correctly, followed the claims script, and showed empathy. Teams get a full picture of agent performance instead of a guess based on a handful of calls. Level AI's quality assurance software does this for 100% of interactions and still lets a human reviewer override any score.

7. Real-Time Compliance Monitoring

Insurance is a regulated business. Agents must read certain disclosures, collect consent, ask required underwriting questions, and follow state rules for claims handling. One missed line can mean a complaint or a fine.

AI regulatory compliance monitoring checks each conversation against those rules. It can flag a missing disclosure while the call is still active, so the agent can correct it, or alert a supervisor right after. Compliance teams also get a searchable record of every check, which makes audits far less painful. Pairing this with screen recording for process adherence shows not just what the agent said but what they did in your systems.

8. Real-Time AI Agent Assist

New and experienced agents alike face hard calls: a claim denial, an upset customer, or a policy rule they have not seen before. Searching knowledge articles while a customer waits is stressful.

Agent assist listens to the live call and shows the agent the right answer, the next step, or the required disclosure at the moment they need it. Notes and call summaries are written automatically after the call. This guide to real-time agent assist shows how it works in practice. Fewer searches mean shorter calls and more consistent answers.

9. Insurance Fraud Detection

Fraud is not only a claims problem. It also happens on the phone. Callers pretend to be policyholders, and in some cases a group of callers keeps trying to reach one specific agent who they believe will approve a refund or claim without enough checks.

AI can spot these signs by reading every conversation instead of a sample. It looks for patterns such as repeated identity failures, the same phrases used across many calls, unusual requests for refunds, or callers who ask for a particular agent. When it finds one, it flags the interaction for the investigation team in real time. AI does not decide that a claim is fraud. It points investigators to the right conversations faster. Speech analytics (technology that turns spoken words into data you can search and measure) is what makes this kind of detection possible at scale.

10. Voice of the Customer (VoC) Analytics

Every call holds a clue about what customers think. Alone, one call tells you little. Across thousands, patterns appear: a confusing renewal letter, a claim step that causes repeat calls, or a competitor customers keep mentioning.

Voice of the customer analytics groups conversations by topic and sentiment so leaders can see what drives calls, complaints, and cancellations. Insurers use these insights to fix confusing processes, update scripts, and reduce avoidable calls before they pile up.

11. Intelligent Underwriting and Risk Assessment

Underwriting is the process of deciding whether to offer coverage and at what price. It depends on gathering and reading a lot of information, which is slow when done by hand.

AI speeds this up by collecting applicant details through conversation, reading documents, and highlighting risk factors for the underwriter to review. Predictive models can add a risk score. The underwriter still makes the call. Conversation data helps here too. Call analytics can confirm that agents ask every required underwriting question and surface patterns, such as common answers that point to higher risk.

How Did One Insurance Provider Use AI in Its Contact Center?

A provider of device insurance, warranty, and support services faced a familiar problem. It had plenty of contact center data but few insights, and its keyword-based quality scores did not reflect what customers actually experienced.

After partnering with Level AI, the company improved QA score accuracy, used Voice of the Customer analytics to find trending pain points, and gave agents real-time guidance. As a result, average handle time dropped 20% and first call resolution rose 15%. You can read the full insurance provider case study for the details.

If you are comparing platforms, this overview of the best insurance call center software shows what to look for.

How Level AI Uses AI to Transform Insurance Operations

Insurance companies handle high volumes of customer calls about policy details, claims status, premium payments, and coverage questions. Level AI Voice Agents can help automate routine customer interactions, answer common inquiries, and route complex issues to the appropriate human agents. This allows insurers to manage repetitive support requests more efficiently while enabling their teams to focus on conversations that require human judgment.

Beyond voice automation, Level AI helps insurers improve service quality and operational efficiency through automated quality assurance, real-time compliance monitoring, and AI-powered agent assistance. The platform analyzes customer interactions to identify potential compliance gaps, evaluate agent performance, flag suspicious conversational patterns for further investigation, and uncover recurring customer issues through Voice of the Customer analytics. These insights help insurance teams improve agent performance, strengthen quality oversight, and identify opportunities to streamline customer support.

Ready to See These AI Use Cases Work for Your Insurance Team?

See how Level AI automates routine calls and scores every conversation across your insurance operation.

Schedule a demo

Ready to See These AI Use Cases Work for Your Insurance Team?

See how Level AI automates routine calls and scores every conversation across your insurance operation.

Schedule a demo