Artificial Intelligence
Time Series
Technology
April 30, 2025
AI and the new frontiers in the insurance sector
Data science techniques improve prediction accuracy, optimize processes, and create fairer and more sustainable products
The current era is based on data analysis, which imposes unprecedented challenges and opportunities for the insurance sector. Traditional factors continue to influence claims and policy pricing, while new technologies, such as artificial intelligence, remote sensing and telematics data analysis, transform risk assessment and management. This article explores recent advances in claims and pricing insurance modeling, focusing on how data science techniques improve prediction accuracy, optimize processes, and create fairer and more sustainable products.
The insurance industry has always been linked to statistical analysis. For decades, policy pricing and claims forecasting were based on traditional models such as linear regressions and generalized linear models (GLM), which offer high interpretability in scenarios with linear relationships between variables.
However, the complexity of modern risks, driven by climate change, new mobility patterns, and technological transformations, has highlighted the limitations of conventional methods. Many phenomena began to require models capable of capturing complex relationships and hidden patterns in the data.
In this context, techniques such as Random Forest, XGBoost, and deep neural networks began to be incorporated into the claims prediction process, offering relevant gains in accuracy. Another important movement was the expansion of data sources, including telemetry, satellite imagery, and climate data to enrich risk models.
The widespread adoption of these methodologies still faces challenges such as the need to ensure model interpretability, protect sensitive data, and adapt practices to regulatory requirements.
Car insurance
The automotive insurance sector has been one of the most dynamic in adopting new technologies. With detailed telematics data on speed, acceleration, and driving patterns, insurers have a richer basis for understanding driver behavior.
Recent studies show that the integration of telematics data surpasses traditional variables in accident prediction. Gao, Meng, and Wüthrich (2019) demonstrated that variables derived from speed and acceleration heatmaps were more effective than demographic factors in modeling claim frequency. Subsequently, Gao, Wang, and Wüthrich (2022) showed that combining this data with machine learning significantly improves predictive accuracy.
Yu et al. (2021) used neural networks optimized by genetic algorithms to predict automotive claims, achieving accuracy above 95%. Decision tree-based models have also consolidated as robust alternatives, with Hanafy and Ming (2021) demonstrating that Random Forest obtained 86.77% accuracy in predicting claims.
The search for models that combine accuracy with interpretability has led to architectures such as TabNet. McDonnell et al. (2023) observed that TabNet outperformed traditional models in recall and F1-score, offering a balance between performance and transparency.
The sector also incorporates alternative sources of information, such as social networks, which can increase the accuracy of automotive warranty request prediction by up to 21.9% (Shokouhyar et al., 2021).
Agricultural insurance
In rural insurance, information asymmetry represents a significant challenge. Climate unpredictability and the difficulty of large-scale monitoring limit the efficiency of traditional models. In this scenario, the combination of artificial intelligence with remote sensing emerges as an innovative solution.
Barros and Freitas (2023) integrated satellite imagery with machine learning algorithms to predict agricultural claims. Analyzing over 9,500 contracts in Paraná, the study identified Random Forest as the most effective model, with an accuracy of 71.35%.
The differential of this approach lies in the reduction of informational asymmetries. With satellite images and adequate predictive models, it is possible to continuously monitor crops and anticipate risks with greater precision than traditional methods.
This methodology also promotes more inclusive practices, allowing small and medium-sized producers to be evaluated more fairly, based on objective evidence.
Life insurance and pension
The life insurance and pension segment also incorporates advances in risk modeling. These products require long-term forecasts, sensitive to demographic and economic changes.
Gonçalves and Pandolfi (2024) demonstrated the effectiveness of models of time series by comparing linear regression and ARIMA models. Using ten years of data from major Brazilian insurance companies, they concluded that the regression model showed a lower mean squared error, proving to be slightly superior.
Já Neves, Fernandes e Melo (2014) proposed a more elaborate statistical model for predicting investment redemption rates, combining different techniques: they used generalized linear models (GLM) to understand how explanatory variables (those that have the potential to influence or predict the response of an experiment) influence redemptions, ARMA-GARCH processes to simultaneously model the trend and volatility of these rates over time, and elliptical copulas to analyze the relationship between redemptions and financial market performance. This approach allowed them to observe that redemption rates increase when the stock market performs worse, indicating an inverse correlation. The study reinforced the idea that sophisticated statistical modeling is essential for adequately assessing the financial risks associated with these products.
Marine and climate insurance
In the marine insurance sector, data integration in real-time has become essential. Operations in dynamic environments are impacted by weather conditions and rapidly changing factors, requiring models capable of capturing this volatility.
Adland et al. (2021) demonstrated the potential of meteorological data combined with records from the Automatic Identification System of Ships to predict naval accidents. Analyzing over 42,000 voyages in the North Pacific, they employed models such as LASSO regression and XGBoost, showing that the inclusion of this data significantly improved predictive capability.
This approach represents a qualitative leap compared to traditional methodologies. With high-frequency data, it is possible to adjust prices more precisely and implement risk mitigation measures in real-time. These practices have potential application for other types of insurance affected by environmental variables, pointing to a future with more adaptive pricing and management.
Trends and challenges
The technological transformation in the insurance sector points to a dynamic and challenging future. The adoption of advanced models offers gains in efficiency and precision, but issues arise related to ethics, governance of data and regulation.
A promising trend is the development of discrimination free models. Lindholm et al. (2024) proposed multitask neural networks to calculate prices without using sensitive variables or their proxies, ensuring that the algorithms do not perpetuate historical biases .
Another challenge is the balance between personalization and predictive robustness. Hosein (2024) highlighted that, although personalization brings competitive advantages, it increases the risk of overfitting (when the model adjusts too much to the training data, losing its ability to generalize to new data) and can reduce the robustness of the models in out-of-sample scenarios.
The interpretability of models also becomes increasingly relevant. Solutions like TabNet demonstrate that it is possible to develop accurate models without sacrificing the ability to explain predictive decisions.
The use of alternative data sources requires careful management of privacy and security of information. Compliance with regulations such as LGPD and GDPR will be increasingly central to the sustainability of data-driven business models. In summary, the next frontier for the sector will not only be technological, but also ethical and regulatory.
The evidence presented shows that the combination between data science and technological innovation generates substantial gains in precision, efficiency, and sustainability. The sector is advancing towards more personalized, fair, and resilient practices.
However, technical advancements bring new challenges. The need to ensure algorithmic equity, data protection, transparency of models, and regulatory compliance imposes a strategic agenda beyond mere technology adoption. Innovating means not only improving predictive capability but building ethical and responsible business models.
In an increasingly competitive and data-driven market, insurers that can align technological innovation with solid governance practices will have a significant strategic advantage.
Who wrote this column
José Erasmo Silva
