Deep Learning in Insurance: AI for Risk Assessment

News

Commentary

Estimates regarding the economic impact of artificial intelligence typically operate in a realm of high uncertainty. According to Minsait, a company spun off from Indra, the AI sector could reach up to 25% of global GDP in the coming decades—a projection based on its experience in the insurance industry. Beyond the macroeconomic figure—whose accuracy is inherently speculative—the relevant aspect of this news is the sectoral context supporting it and the technology enabling it: Deep Learning. It is claimed that "Today, six out of ten insurance companies have already implemented artificial intelligence projects, most using Deep-Learning platforms". This figure, unlike the GDP projection, is verifiable and reflects a genuine adoption process. The insurance sector, traditionally based on historical data aggregation and established statistical models, is being transformed by the capacity of Deep Learning to process vast volumes of unstructured information and detect patterns beyond the reach of traditional methods.

What is Deep Learning?

"Deep Learning" is a subfield of machine learning that focuses on training algorithms known as artificial neural networks to perform specific tasks without requiring detailed programming. It employs multi-layered neural network structures (hence the term "deep") to learn and represent data in a hierarchical manner. Through these layers, neural networks can automatically learn complex features and patterns from large datasets.

Unlike classical machine learning approaches, which required human experts to manually define relevant variables (feature engineering), deep learning extracts these features autonomously during training. This capability has made it especially effective in domains such as speech recognition, computer vision, and natural language processing—all areas with direct applications in the insurance sector.

Rather than relying on manual feature engineering, as in some traditional machine learning methods, deep learning enables the model to automatically learn the most relevant features during training. This has led to significant advances in areas such as speech recognition, computer vision, natural language processing, and other complex tasks where pattern detection and feature representation are fundamental.

Applications in the Insurance Sector

For example, in the insurance sector, Artificial Intelligence combined with Deep Learning assists in the risk assessment process for automobile insurance policies. This is achieved through the analysis of data such as driving histories, geographic locations, traffic conditions, and more. These models can automatically learn patterns indicative of higher or lower risk, enabling insurers to adjust premiums more accurately. Risk assessment is one of the core functions of insurance companies. Traditionally, it relied on linear models built upon predefined variables (age, vehicle type, zip code, etc.). Deep learning enables the incorporation of a much larger number of variables—including unstructured data such as accident images or claim texts—and captures complex interactions among them that traditional models cannot represent. In fraud detection, Deep Learning can analyze subtle patterns in claims and insured behavior to identify potential fraudulent activities. For instance, by analyzing large datasets, a Deep Learning model can detect anomalies in statements and text written by the insured that may indicate a false claim.

Fraud in the insurance sector represents a significant cost that ultimately affects premiums for all policyholders. Deep learning-based systems can analyze the language of claims, the temporal consistency of reported events, or their alignment with known fraud patterns with a sensitivity surpassing that of rule-based systems.

Benefits and Risks

These deep-learning applications enable insurance companies to make more informed decisions and improve the accuracy of risk assessment, benefiting both insurers and policyholders by contributing to a fairer and more efficient allocation of premiums and reducing the risk of fraud. However, they also raise other problems and unresolved issues, such as access to personal data and individuals' right to privacy, as well as legislation concerning data protection.

Deep-learning-based risk analysis requires precisely access to large volumes of personal data—driving histories, geolocation, behavior—whose collection and processing are subject to regulations such as the General Data Protection Regulation (GDPR) in Europe. The tension between predictive accuracy and respect for privacy is particularly pronounced here. Added to this is the risk that models may perpetuate or amplify existing biases if the training data reflect historical discriminations.