Insurance sector AI applications

Artificial intelligence demonstrates the important potential to transform the operations of the insurance sector and create important added value.

Its applications cover a variety of risk underwriting and pricing, billing management, fraud detection and prevention, and customer relationships.

Automating Underwriting and Pricing Operations

AI is fundamentally transforming the insurance sector by automating and optimizing two critical processes: risk underwriting and pricing.

Artificial intelligence allows for automated underwriting by analyzing huge amounts of data in real time (claim history, social media, geolocation data) to more accurately assess risks. This ability to reduce human bias allows you to select the best coverage and set premiums paid in record time.

Internal Process Optimization

Artificial intelligence and robotics process automation (RPA) are transforming the management of recurring tasks in the insurance industry. RPA handles routine tasks, but AI can analyze and understand them, enabling more advanced automation.

This combination reduces manual workloads and increases the efficiency and accuracy of operations such as processing, billing, and policy underwriting.

Robots, which are enhanced by AI through technologies such as optical character recognition (OCR) and natural language processing (NLP), can extract, capture and interpret data, allowing them to determine the right process. This intelligent automation reduces processing time, development costs and risk of errors.

Tasks performed by RPA

• Input and Transfer Data: Copy and Paste information between systems;
• Running structured workflows: Automatic handling of subscription requests based on fixed criteria;
• Generating standardized documents: issuing contracts or certificates;
• Validate required fields: Make sure the form is fully completed,
• Data adjustment: Customer list adjustments between two databases
• Database updates.

Tasks performed by AI (learning, analysis, decision making)

• Extract data from invoices, identity documents, and more through document recognition via OCR.
• Natural Language Processing (NLP): Analysis of written or voice complaints;
• Automatic classification: sorting emails or messages according to specific criteria such as “requesting information” or “complaints”;
• Anomaly detection: Identifying fraud using predictive models;
• Decision-making: Automatic approval of small bill reimbursement based on learning criteria.

This combination of AI and RPA appeals to many players in the sector, as demonstrated by the following initiatives:

• Alan, a French health insurance company, offers its clients (companies) the option to integrate the platform into the HR Information System (HRI) for the purpose of managing health and life insurance.

Thanks to this partnership, AI-RPA automates the processing of redemption claims. This approach allows businesses to reduce their time spent managing their health insurance to less than two hours a year.

AI revolutionizes the insurers' ability to accurately understand the needs of individual clients, analyzing demographics, behavioral and historical data.

Many insurers have integrated AI to improve risk analysis and use vast amounts of training data to improve the accuracy of their actuarial models.

The technology also paves the way for more personalized insurance and faster claim processing, thereby optimizing the entire insurance process.

• French Generals Use AI to analyze customer data to design customized insurance products, including digital interactions, purchase history, and consumption habits.
• axa In collaboration with Microsoft, we have developed an internally generated AI platform called Secure GPT. We can coordinate and provide services such as complementary medical and preventive advice, recommended based on customer history or risk profiles.
• progressivethe second-largest car insurance company in the United States uses machine learning algorithms to analyze historical data and driving behavior. This approach can improve risk assessments and allow the company to provide fairer and personalized policies and rates, thereby increasing customer satisfaction.

Asserts management in the age of artificial intelligence

Claim management refers to all measures taken by the insurance company when the policyholder reports a claim. This process includes reporting claims, processing files, assessing damages, and paying compensation.

Ai enabled:

• Automating claim reporting processes through mobile apps or chatbots;
• Analysis of claim data: photos, text,
• Estimate repair costs

Fraud detection and prevention

Insurance fraud accounts for 10% to 15% of total revenue in the global insurance market. This phenomenon is an increased risk, particularly with the annual increase in global premiums, which reached USD 718.6 billion in 2023.

A 2025 study published by Deloitte noted that increasing premiums in the US to offset fraud-related losses is not a viable long-term strategy. This report proposes two approaches to combat this tragedy.

• Proactively combat fraud using traditional rule-based detection methods and advanced preventive techniques;
• Use generated AI to prevent fraud attempts. According to Deloitte, 35% of insurance executives believe fraud detection is one of the top five areas of generating AI applications for the next 12 months.

In the US, insurers' annual losses reached USD 122 billion in 2024, as an estimated 10% of claims are fraudulent.

By 2032, insurers were able to reduce this loss from 80 to USD 160 billion by adopting AI-driven technology to process claims, according to Deloitte.

Continuous customer support

A chatbot is a virtual assistant that interacts with customers using artificial intelligence algorithms. Designed to be user-friendly, it can guide users, answer questions and process a variety of requests.

Open 24/7, these tools reduce operational costs by 30%-40% compared to traditional customer service. Efficiently automate repetitive tasks, such as providing information and handling simple complaints, and process multiple requests simultaneously. This feature improves response time by 35% and optimizes the customer experience.

From 2024 to 2025, the innovation in the insurance sector's chatbots focused primarily on the integration of Generated AI (GENAI) and ongoing advancements in natural language processing (NLP). This task is no longer limited to answering frequently asked questions, but now we aim to provide more natural, personalized, and positive interactions.

Among the most famous chatbots are

Maya and the gym

These are chatbots from American auto insurance company Lemonade, which uses AI to simplify policy underwriting and management.

• Maya A chatbot that guides users through the process of acquiring insurance contracts. Ask what your prospects are answering to in order to personalize your offers in real time.
• Gym They have different roles. He manages claims and refunds. He interacts with the insured by asking questions in simple language and adapts the survey to the questionnaire. Jim then moves to request claim photos, videos and other additional documents before using AI to process the data. From then on, the billing refund may be immediate, depending on the speed of the billing process.

Zara

The chatbot was developed by insurance company Zurich (Switzerland) in collaboration with startup SPIXII, which specializes in conversation assistants with artificial intelligence. Its main purpose is to help customers submit claims and respond to requests.

Once operations occurred, the chatbot handled 765 customer interactions, accounting for 20% of the amount of refund requests in the first six weeks. Autonomously process 35% of billing, reduce processing time by 30%, and achieve 85% customer satisfaction.

Similar experience has been adopted by several other insurers and reinsurers.