AI and insurance: how artificial intelligence is revolutionising the sector
Artificial intelligence is profoundly transforming the insurance sector. From process automation to fraud detection and offer personalisation, AI is establishing itself as a strategic lever for insurers and brokers. But this technological revolution also raises essential questions: how can these tools be integrated while ensuring data protection? What are the real benefits for customers and professionals? This article provides concrete answers and actionable insights to understand the impact of AI insurance on your business.
How AI is transforming insurance professions
Artificial intelligence in insurance refers to all the technologies that enable machines to analyse data, learn autonomously and automate complex tasks. Machine learning, predictive algorithms and generative AI are at the heart of this transformation.
For insurers, AI represents a unique opportunity to optimise risk management, improve customer experience and reduce operational costs. Insurance companies now exploit colossal volumes of data to refine their predictions and anticipate risk scenarios with unparalleled precision.
Concretely, AI enables:
- Analysis of thousands of policies and claims in seconds
- Detection of anomalies and fraud attempts
- Personalisation of offers according to the specific needs of each policyholder
- Automation of claims handling and acceleration of settlements
The arrival of generative AI marks a new turning point. These tools can now generate content, analyse complex documents and assist professionals in their daily tasks. The integration of artificial intelligence into business processes now relies on technologies such as MCP (Model Context Protocol), which allows AI models to access company data in a structured and secure manner.
Concrete applications of artificial intelligence in insurance
Automation of risk and claims management
Risk management is at the heart of the insurer's profession. Artificial intelligence enables these risks to be assessed with remarkable precision by analysing historical data, demographic information and customer behaviour. Predictive models identify at-risk profiles and enable insurers to adjust their pricing accordingly.
Business process automation is also transforming claims management. Thanks to AI, claims are processed more quickly: automatic document analysis, instant claim assessment, generation of settlement offers in minutes. For brokers, this automation translates into considerable time savings, enabling professionals to focus on customer support and advice.
Fraud detection through algorithms
Fraud represents a major cost for the insurance sector. Artificial intelligence offers innovative solutions to identify suspicious behaviour and protect companies against financial losses.
AI tools analyse thousands of data points simultaneously to spot inconsistencies in claims. By cross-referencing policy information, customer history and external databases, algorithms detect unusual patterns that would escape the human eye.
This detection power enables insurers to reduce their costs whilst preserving fairness between all policyholders. Real-time analysis, identification of organised fraud networks and reduction of false positives continuously improve performance thanks to machine learning.
Offer personalisation and customer experience
AI is revolutionising the relationship between insurers and their customers. Personalisation is becoming the norm, with bespoke offers that meet the specific expectations of each policyholder.
Chatbots powered by artificial intelligence enable customer service available 24/7. These virtual assistants converse naturally with users, answer their questions and guide them through their procedures.
AI analyses behavioural and demographic data to propose tailored solutions. A young driver will not have the same needs as a pensioner, and artificial intelligence identifies these nuances to create relevant offers. This approach strengthens policyholder loyalty and significantly improves customer experience, a major challenge in an increasingly competitive sector.
Concrete use cases for wholesale and delegated authority brokers
AI to optimise complex risk placement
For a wholesale broker specialising in corporate risks, artificial intelligence radically transforms day-to-day work. Take the example of a placement for an industrial company with multiple risks: property damage, public liability and cyber risks.
Thanks to AI, the broker can now automatically analyse client documents, identify in minutes the most relevant insurers according to their appetite for this type of risk, generate personalised presentations tailored to each company's expectations, and receive alerts on condition changes or new insurer capacities.
This use of AI enables the wholesale broker to handle more cases whilst improving service quality. Time saved on administrative tasks is reinvested in analysing client needs and negotiating with insurers, where human expertise truly makes the difference.
AI supporting portfolio management for delegated authority brokers
For a delegated authority broker managing a portfolio of several thousand policies, artificial intelligence becomes an indispensable business management tool. AI continuously analyses the entire portfolio to identify policies reaching renewal date, clients presenting a lapse risk, cross-selling opportunities and market trends enabling offer adjustments.
Concretely, the delegated authority broker receives each morning an intelligent dashboard that prioritises their actions for the day. This proactive approach, guided by artificial intelligence, enables significant increases in retention rates and average basket per client.
AI tools also facilitate claims management by detecting cases requiring particular attention. AI enables these at-risk situations to be identified and swift intervention to maintain the trust relationship.
Challenges and issues of AI for insurers
Data quality and algorithmic bias
Data quality is a determining factor for the proper functioning of AI in insurance. Incomplete, outdated or erroneous data can lead to strategic decision-making errors with significant financial consequences.
Algorithmic bias represents a major challenge. If the training data for AI models contains historical biases, algorithms will reproduce and amplify these discriminations. In the insurance sector, this can translate into unfair pricing or cover refusals based on inappropriate criteria.
Insurers must implement rigorous control mechanisms: regular database audits, verification of training sample representativeness, non-discrimination testing on models and continuous monitoring of algorithm results. Sector professionals must demand transparent and justifiable AI tools.
Regulatory compliance: GDPR and AI Act
The use of artificial intelligence in insurance raises major regulatory issues. GDPR imposes strict obligations regarding the protection of policyholders' personal data. The AI Act, adopted by the European Union, establishes a specific framework for artificial intelligence with requirements proportionate to the risk level.
For the insurance sector, this means: mandatory transparency on automated decisions, human oversight of critical decisions, complete documentation of AI systems used and regular risk assessment related to the use of these technologies.
Insurance companies must implement robust governance to monitor the entire lifecycle of their AI. France, through players such as Mistral, is developing sovereign AI solutions that integrate these regulatory constraints from their conception.
Necessary technological investments
Integrating AI into the insurance sector requires substantial investments in technological infrastructure. Insurance companies must modernise their information systems to fully exploit the power of artificial intelligence.
For small and medium-sized sector players, these costs can represent a significant barrier. This is why numerous initiatives aim to pool resources and offer accessible AI solutions in service form. Brokers can thus benefit from AI power without prohibitive initial investments.
Return on investment must be assessed over the long term. AI enables operational cost reduction, improved customer satisfaction and optimised risk management. These benefits largely compensate for initial investments for companies adopting a coherent strategy.
AI and skills transformation in the insurance sector
The arrival of artificial intelligence in the insurance sector does not mean job disappearance, but rather their transformation. Professionals must develop new skills to work effectively alongside these technologies.
Today's brokers and insurers need to master AI basics to understand how these tools work and how to exploit them optimally. This skills development involves specific training on data analysis, algorithm understanding and interpretation of AI-generated results. Sector players investing in team training gain a head start on their competitors.
AI frees professionals from repetitive and time-consuming tasks to refocus them on higher value-added activities. A broker who no longer spends hours entering data or manually comparing offers can dedicate this time to developing client relationships, refining advice and prospecting new markets. This evolution towards more strategic and relational professions values human expertise.
Insurance sector companies must support this transition. This involves:
- Implementing continuous training programmes on AI tools
- Recruiting hybrid profiles combining sector expertise and technical skills
- Creating bridges between technical and commercial teams
- Developing a culture of innovation and experimentation
Training initiatives are multiplying in France. Professional bodies offer specific modules on AI applied to insurance. Brokers following these training courses report better understanding of issues and increased ability to dialogue with their clients on these subjects.
This skills transformation also represents an opportunity to make the sector more attractive to young talent. New generations seek professions where technology plays a central role. By modernising their image and tools, insurance players can attract qualified profiles who would previously have chosen other sectors.
Security and protection of AI systems in insurance
The growing use of artificial intelligence in the insurance sector creates new security challenges. AI systems process massive volumes of sensitive data and make decisions that directly impact policyholders. Protecting these systems against cyberattacks and failures becomes an absolute priority for sector professionals.
AI systems are attractive targets for cybercriminals. A successful attack can enable manipulation of pricing algorithms, access to confidential client databases or compromise fraud detection processes. Insurers must implement enhanced security measures to protect their AI infrastructure: data encryption, strict access controls, continuous monitoring of suspicious activities and incident response plans.
AI models themselves can be vulnerable to specific attacks. Adversarial attacks consist of manipulating input data to deceive the algorithm and obtain favourable decisions. In the insurance context, a fraudster could for example subtly modify claim information to bypass automated detection systems. Insurers must develop defence mechanisms against these attack techniques.
AI system security also involves protecting proprietary models. Algorithms developed by insurance companies often represent a significant competitive advantage. Measures must be taken to prevent intellectual property theft and ensure competitors cannot reproduce these models.
Brokers and insurers must also concern themselves with AI system resilience. What happens if a model fails or produces aberrant results? Backup procedures must be established to guarantee service continuity. Professionals must be able to switch to manual processes in case of technical failure, whilst retaining the capability to process urgent cases.
The security of data used to train AI models is equally crucial. If corrupted or malicious data is introduced into training databases, algorithms will produce biased or erroneous results. Insurers must implement rigorous data validation and cleansing processes before their use in AI models.
Transparency and communication: maintaining trust in a sector transformed by AI
The insurance sector rests on a fundamental principle: trust. Policyholders entrust their personal data, financial security and peace of mind to insurers and brokers. The arrival of artificial intelligence naturally raises legitimate questions: how is my data used? Who really makes decisions concerning my policy?
Faced with these questions, sector professionals must adopt transparent and educational communication. This is not simply about complying with legal obligations imposed by GDPR and the AI Act, but truly about building a lasting trust relationship with policyholders.
Best practices include: clearly informing clients about AI tool usage, explaining in accessible terms how algorithms work, guaranteeing that a human always supervises important decisions, offering policyholders the right to request human review of an AI-made decision.
Brokers play an essential role in this transparency approach. As trusted intermediaries, they must be able to explain to their clients how AI improves their service whilst protecting their interests. A broker who masters AI issues can reassure clients by demonstrating that technology reinforces human expertise, not replace it.
Communication around AI must also focus on concrete benefits for customers. Faster claims processing, better-adapted offers, personalised prevention advice: these tangible advantages reinforce AI acceptance by policyholders. Professionals must also be honest about artificial intelligence limitations. This honesty enables client expectation management and avoids disappointments.
The future of AI in insurance: opportunities and prospects
Artificial intelligence opens unprecedented prospects for the insurance sector. Generative AI, illustrated by tools such as those developed by CGI or Mistral, now enables automatic generation of personalised content for policyholders. Simplified policies, bespoke explanations, prevention advice: AI facilitates offer understanding and improves client relationships.
AI models are becoming increasingly sophisticated in their ability to anticipate claims. By analysing weather data, building condition information and societal trends, algorithms can alert policyholders to emerging risks and propose targeted prevention measures.
Embedded insurance particularly benefits from artificial intelligence advances. This distribution channel, which relies on partnerships with brands, distributors or associations, is being transformed by AI. Algorithms analyse purchase data and partner client behaviour to identify opportune moments to propose insurance. For example, smartphone purchase can automatically trigger an adapted insurance offer, personalised according to the buyer's profile and specific needs.
AI also enables embedded product creation optimisation by analysing characteristics of each partner's clients. A sports retailer will have a different clientele from a bank or car manufacturer. Artificial intelligence identifies these specificities to design bespoke offers that precisely meet each segment's expectations. This personalisation considerably improves conversion rates and policyholder satisfaction.
Evolution prospects include real-time dynamic pricing, automatically triggered parametric insurance, enhanced collaboration between insurers through secure data sharing, and emergence of new insurance products covering AI-related risks themselves.
The Model Context Protocol (MCP) plays a key role in these evolutions. By enabling AI models to access insurance company data and systems in a standardised manner, MCP facilitates artificial intelligence integration into existing workflows whilst maintaining data security and compliance.
Korint: AI and MCP serving brokers and insurers
Faced with these transformations, insurance sector professionals need powerful tools to remain competitive. Korint positions itself as an innovative platform that integrates artificial intelligence directly into its modules.
This technical architecture enables Korint to connect AI models to business data securely, guarantee compliance with GDPR and AI Act regulations, offer seamless integration with existing systems and facilitate access to AI functionalities without technical complexity.
Using advanced AI modules, Korint enables wholesale brokers, delegated authority brokers and insurers to automate repetitive tasks, improve decision-making through intelligent data analysis, personalise customer experience and optimise risk management and fraud detection.
This approach enables sector players to focus on their core business: advice and client support. AI becomes a daily ally, capable of increasing performance without replacing human expertise.
Artificial intelligence is no longer an option for the insurance sector. It establishes itself as an essential lever for competitiveness, efficiency and customer satisfaction. For wholesale brokers, delegated authority brokers and insurers, understanding these issues and acting accordingly becomes a strategic priority. Protect your future by adopting now an informed approach to AI insurance, with solutions such as Korint that facilitate this technological transition through Model Context Protocol integration.