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Artificial intelligence showcases significant potential to transform insurance sector operations and create significant added value.
Its applications are varied, covering risk underwriting and pricing, claims management, fraud detection and prevention, and customer relations.
Automation of underwriting and pricing operations
AI is radically transforming the insurance sector by automating and optimizing two key processes: risk underwriting and pricing.
Artificial intelligence makes it possible to automate underwriting by analyzing massive amounts of data in real time (claims history, social media, geolocation data) to assess risk more accurately. This ability to reduce human bias makes it possible to choose the best coverage and set the premium to be paid in record time.
Optimization of internal processes
Artificial intelligence and robotic process automation (RPA) are transforming the management of repetitive tasks in the insurance industry. While RPA handles routine tasks, AI empowers it to analyze and understand, permitting more advanced automation.
This combination reduces manual workload while improving the efficiency and accuracy of operations such as processing inquiries, claims, or policy underwriting.
A robot enhanced by AI through technologies such as optical character recognition (OCR) or natural language processing (NLP) can extract, capture, and interpret data, then determine the appropriate process. This intelligent automation reduces processing times, development costs, and the risk of error.
Tasks performed by RPA
- Data entry and transfer: copying and pasting information between systems,
- Execution of structured workflows: automated processing of a subscription request based on fixed criteria,
- Generation of standardized documents: issuing contracts or certificates,
- Verification of mandatory fields: checking that a form has been completed in full,
- Data reconciliation: reconciling customer lists between two databases,
- Updating the database.
Tasks performed by AI (learning, analysis, and decision-making)
- Document recognition via OCR to extract data from invoices, identity documents, etc.,
- Natural language processing (NLP): analysis of written or voice complaints,
- Automatic classification: sorting emails or messages according to certain criteria such as “request for information” or “complaint”,
- Anomaly detection: identifying fraud using predictive models,
- Decision-making: automatic approval of small claims reimbursements based on learned criteria.
This combination of AI and RPA appeals to many players in the sector, as substantiated by the following initiative:
- The insurer Alan, a French health insurance company, offers its customers (companies) the option of integrating its platform into their human resources information systems (HRIS) for the purpose of managing their health and life insurance.
Thanks to this partnership, AI-RPA automates the processing of reimbursement claims. This approach allows companies to reduce the time spent managing their health insurance to less than two hours per year.
AI is revolutionizing insurers' ability to accurately understand individual customer needs, analyzing demographic, behavioral, and historical data.
Many insurance companies are integrating AI to refine risk analysis and improve the accuracy of actuarial models using vast amounts of training data.
This technology also paves the way for more personalized insurance offerings and faster claims processing, thereby optimizing the entire insurance process.
- Generali France uses AI to analyze customer data to design customized insurance products: digital interactions, purchase histories, consumption habits.
- AXA has developed, in partnership with Microsoft, an internal generative AI platform called Secure GPT. It allows offers to be adjusted and services such as complementary medical care or prevention advice to be recommended based on customers' history or risk profile.
- Progressive, the second-largest motor insurer in the US, uses machine learning algorithms to analyze historical data and driving behavior. This approach refines risk assessment and enables the company to offer fairer, more personalized policies and rates, thereby increasing customer satisfaction.
Claims management in the era of artificial intelligence
Claims management refers to all the steps taken by an insurer when a policyholder reports a claim. This process includes reporting the claim, processing the file, assessing the damage, and paying compensation.
AI enables:
- automation of the claim reporting process via a mobile app or chatbot,
- analysis of claim data: photos, text,
- estimation of the cost of repairs
Fraud detection and prevention
Insurance fraud accounts for 10% to 15% of the total turnover of the global insurance market. This phenomenon is a growing risk, particularly due to the annual increase in global premiums, which reached 7186 billion USD in 2023.
A study published in 2025 by Deloitte pointed out that increasing premiums in the United States to offset losses related to fraud is not a viable long-term strategy. The report proposes two approaches to combat this scourge:
- Proactively combating fraud using traditional rule-based detection methods and advanced prevention techniques,
- Using generative AI to prevent fraud attempts. According to Deloitte, 35% of insurance executives consider fraud detection to be one of the top five areas of application for generative AI over the next 12 months.
In the United States, with an estimated 10% of claims being fraudulent, the annual loss to insurers reached 122 billion USD in 2024.
According to Deloitte, insurers could reduce this loss by 80 to 160 billion USD by 2032 by adopting AI-driven technologies for claims processing.
Continuous customer support
Chatbots are virtual assistants that use artificial intelligence algorithms to interact with customers. Designed to be user-friendly, they can guide users, answer their questions, and handle various requests.
Available 24/7, these tools reduce operating costs by 30% to 40% compared to traditional customer service. They efficiently automate repetitive tasks, such as providing information or handling simple complaints, and can handle multiple requests simultaneously. This capability improves response times by 35%, optimizing customer experience.
During 2024-2025, innovation in chatbots in the insurance sector focused primarily on the integration of generative AI (GenAI) and continued progress in natural language processing (NLP). The challenge is no longer limited to answering frequently asked questions, but now aims to offer more natural, personalized, and proactive interactions.
Among the best-known chatbots are:
Maya and Jim
These are chatbots from the American motor insurer Lemonade, which uses AI to simplify policy underwriting and management.
- Maya is the chatbot that guides users through the process of taking out an insurance policy. It asks questions that the prospect answers in order to personalize their offer in real time.
- Jim has a different role: he manages claims and reimbursements. He interacts with the insured by asking questions in simple language and adapts his questionnaire to the claim in question. Then, Jim moves on to request photos or videos of the claim as well as other additional documents before processing the data using AI. From then on, with the speed of claim processing, reimbursement for the claim can be immediate.
Zara
This chatbot was developed by the insurer Zurich (Switzerland) in partnership with the startup Spixii, which specializes in conversational assistants powered by artificial intelligence. Its main objective is to assist customers in filing their claims and responding to their requests.
Once operational, the chatbot handled 765 customer interactions, representing 20% of the volume of reimbursement requests during the first six weeks. It handles 35% of claims autonomously, reduces processing time by 30% and achieves a customer satisfaction rate of 85%.
Similar experiences have also been adopted by several other insurers and reinsurers.
