Ensuring the effectiveness of artificial intelligence in insurance operations

AI has been part of the insurance sector for years – the finance function in many companies is often the first to be automated. But what is striking in the case of AI is the extent to which the technology is directly integrated into daily operational work. Not only does AI remain in the background as a specialized modeling capability, it is now being used in places where insurance companies spend most of their time and money: handling claims, underwriting, and running complex software.

Industry giants Allianz, Zurichand Aviva It has published guides in just the last 12 months detailing its transition from experimental phases to production tools that support frontline workers in real workflows.

Simple claims: Fewer administrative bottlenecks

Claims processes are a natural testing ground for AI because they consist of a mixture of paperwork and human judgment, and are typically conducted in a time-pressed environment. Allianz describes its Insurance Copilot software as an AI-powered tool that helps claims processors automate repetitive tasks and collect relevant information that might require multiple searches on different systems.

There has been a noticeable change in workflow, Allianz explains. The co-pilot begins by collecting data and summarizing the claim and contract details so the handler can quickly get just the basics down. The algorithm then analyzes the documents, processes that include interpreting agreements and comparing claims to policy details. The tool identifies discrepancies and suggests next steps. Once the human operator has made his decision, the co-pilot helps craft context-aware emails.

This is the type of daily activity that insurance companies care about, and by using their AI tools, they get lower response time, smoother settlements, and reduced friction between employees and customers. Allianz also frames AI as a way to reduce unnecessary payments by highlighting important factors that adjusters may overlook. This has a clear impact on the company’s overall bottom line.

Complex documentation for usable decisions

The quality of underwriting is determined by the quality of information available. Aviva uses the example of insurers needing to read GP medical reports. The company says it is launching an AI-powered summarization tool that uses genAI to analyze and summarize these reports, which can sometimes run into dozens of pages of medical texts. Artificial intelligence jobs Allow insurance companies Make faster, more informed decisions.

The immediate value here is not that AI replaces the guarantor, but rather technology that reduces the time you spend reading. The insurer is upfront that underwriters will review the summaries and make the final decision – not the AI. This distinction is important because underwriting is technical and sensitive; Compressing documents into decision-ready summaries can speed up processing, but it also raises questions about accuracy, omissions, and auditability. Aviva addresses this by noting “Strict tests and controlsThe company says that the active testing phase processed about 1,000 cases before implementation began to ensure the required standards.

Uncertain contracts and services in multinational programmes

Commercial insurance is an area with its own challenges, which include the complexity of operating in multiple jurisdictions, and regional differences between policies and stakeholders. Zurich says AI’s ability to process unstructured information allows multinational insurance to operate more easily across multiple countries, helping it build faster and more accurate pictures of commercial insurance offerings. Simplify submissions In different countries.

Zurich also highlights contract assurance as a practical outcome: multinational programs involve multi-layered documentation, diverse local requirements, and have a widespread need for constant scrutiny. She says GenAI helps internal experts compare, summarize and verify coverage in a program using the operator’s native language, “in a fraction of the time” compared to the manual effort required to translate and capture the nuances of international variations. Although not customer-related, genAI improves company responsiveness by allowing underwriters, risk engineers, and claims professionals to work more efficiently.

Zurich also points to AI that “connects the dots,” capable of spotting trends in data that — given the amount of information — go unnoticed by human employees. In fact, AI amplifies rather than displaces the judgment of its experts.

The common denominator here is: augmentation, not automation for the sake of automation

Through these three examples, a consistent pattern emerges:

• AI handles the heavy lifting of reading, researching and drafting; Large tasks in insurance operations.
• Humans remain responsible for subsequent decisions, whether claims payments or underwriting acceptance. (Allianz describes a “human-in-the-loop” approach, and Aviva and Zurich similarly emphasize that experts retain control of the decision-making process.)
• Operational control and scalability are treated as key concerns: pilots, testing, domain-by-field tuning and expansion across business lines are integral to the narrative.

What does this mean for the sector?

Insurers see faster cycle times, better consistency, less manual work, and a path to scale. Their challenge is to implement the tools responsibly, which is defined by safe handling of data, explainability when needed, and training teams so they can appropriately hold the outputs accountable.

AI has become less of a headline in this sector and more of a daily reality, a practical colleague in the routine work of insurance profitability.

(Image source: “housefire” by peteSwede licensed under CC BY 2.0.)

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