Insurance and artificial intelligence: a strategic necessity

Artificial intelligence (AI) is a major technological breakthrough that is gradually transforming all economic activities, including insurance.

Characterized by its speed and versatility, this tool is particularly effective for data management and analysis, performing operations in seconds that previously required several hours of work.

Thanks to its ability to process large volumes of data, make decisions, and automate processes, artificial intelligence enables insurers to reinvent their operations and develop better solutions for risk management. AI is, therefore, contributing to the modernization of the insurance industry, promoting innovation, service personalization, improved operational efficiency, and customer experience.

Today, the use of AI is essential for any insurer seeking to remain competitive in an environment where responsiveness is crucial. AI is becoming a lever for transformation, offering its users unprecedented opportunities.

Audit firm KPMG estimates the AI market in insurance at 79 billion USD by 2032.

Artificial intelligence in insurance

Artificial intelligence in the insurance sector refers to all IT technologies capable of simulating certain human functions such as learning, decision-making, and text, image, or voice recognition.

These technologies enable insurance companies to automatically process large amounts of data, improve the quality of services offered to customers, and strengthen risk management.

Thanks to AI, it is possible, for example, to:

• set fairer rates by analyzing the behavior of policyholders,
• speed up claims reimbursement,
• detect fraud by identifying anomalies in files,
• or even communicate with customers via intelligent virtual assistants.

AI has, therefore, profoundly transformed the insurance business. It helps to make better decisions, faster and more reliably, without completely replacing humans.

Artificial intelligence in insurance services

It took several decades for artificial intelligence to transform the insurance industry. It has developed gradually over the past 70 years, evolving from a simple automation system to cognitive solutions capable of predicting, personalizing, and optimizing decisions in real time.

The early days (1950-1980): automation and expert systems

In the 1950s, AI emerged with fundamental concepts such as the Turing test ⁽¹⁾ and the first neural networks. Financial institutions began experimenting with expert systems to:

• analyze credit,
• detect basic fraud,
• automate repetitive tasks,
• establish simple statistical models for risk assessment.

At that time, artificial intelligence remained dependent on the computing power of machines and the availability of data.

⁽¹⁾ Method proposed by Alan Turing, British mathematician and computer scientist, to assess a machine's ability to mimic human intelligence.

Le Machine Learning (1990-2000)

The 1990s had marked a turning point with the adoption of Machine Learning (ML) or automatic learning, with AI enabling computer systems to learn and improve based on data.

• Fraud detection: algorithms to identify suspicious credit card transactions,
• Automated loan scoring: decision trees, neural networks,
• Algorithmic trading: first high-frequency trading systems.

In the 2000s, insurers used ML for dynamic risk pricing, a solution that allowed them to adjust motor insurance premiums based on driver behavior. During this same period, the first chatbots appeared.

Le Deep Learning (2010-2020)

The 2010s had witnessed the explosion of deep learning using deep artificial neural networks to learn from large amounts of data.

Deep learning enables analysis of unstructured data such as text, images, and voice, which allows for:

• Advanced fraud detection: neural networks detecting complex patterns,
• Automated regulatory compliance: anti-money laundering,
• Automated underwriting: evaluation of underwriting applications in seconds,
• AI-based claims management: image analysis to estimate damage,
• Personalized pricing: predictive models adjusting premiums in real time.

During this period, we also witnessed the emergence of Robo-advisors for automated portfolio management and intelligent chatbots.

At the end of the 2010s, the convergence between AI and blockchain had accelerated the arrival on the market of other software such as smart contracts and automated KYC (Know Your Customer) processes used by companies and financial institutions to verify the identity of their customers.

The era of generative and hyper-personalized AI (2020 to present)

Generative artificial intelligence is a branch of AI that focuses on the autonomous creation of new content such as text, images, music, and video.

Since 2020, generative AI has been revolutionizing customer interaction with advanced virtual assistants. On the other hand, enhanced predictive analytics is developing even further for better asset management and accurate climate risk assessment.