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The integration of artificial intelligence (AI) into safety-relevant applications has increased considerably in recent years, including in the areas of fire protection and property insurance. The application of artificial intelligence opens new possibilities for risk identification, assessment, and minimization, as well as for more efficient claims settlement. AI‑based technologies – such as machine learning, image and speech recognition, or sensor-based data analysis – enable the automated evaluation of large volumes of data from a wide variety of sources in real time. These sources include sensor data, weather information, and building structures and thus support both the early detection of potential hazards and refining projected loss probabilities. This promises a significant improvement in monitoring and response mechanisms. Examples of these improvements may be through learning algorithms to detect fire incidents or adaptive control of fire protection systems.
In addition, AI enables the optimization of insurance processes, thus new forms of pricing, claims settlement, and fraud detection can be developed alongside more precise claims analyses.
This article presents examples of the potential for and challenges of using artificial intelligence in fire protection and property insurance. Both technological and economic aspects are considered, thereby offering a comprehensive understanding of the opportunities and challenges of using AI in this context.
The term artificial intelligence (AI) refers to the development of computer algorithms and systems based on artificial neural computer networks that make it possible to interpret different data sources such as images, sounds, texts, tables, or time series, and to extract information or patterns from those data sources to apply them to unknown data. Artificial neural networks are the central element in deep learning (particularly deep network structures). They are able to perform tasks that would normally require human intelligence. The term AI comprises a variety of technologies and methods, in particular:
AI algorithms can, for example, recognize patterns, understand language, make decisions, solve problems, and learn and improve independently; that is, AI systems have at least a certain degree of autonomy, adaptability, and ability to draw conclusions. The effectiveness of AI systems depends largely on the quality and quantity of the available data, which must be data protection compliant, particularly with regard to the EU General Data Protection Regulation (GDPR).
Smart buildings are increasingly being equipped with a wide range of intelligent, AI‑supported infrastructure control technologies and networked with each other. In AI‑supported smart buildings, for example, AI takes over the analysis and evaluation of incoming data streams and the control of necessary measures, such as triggering a fire alarm in the event of an incipient fire, activating fire protection equipment (fire dampers, fire protection closures, smoke and heat extraction systems), and initiating evacuation measures. Other examples include video surveillance systems that use AI technology to detect smoke development or systems that dynamically enable safe evacuation routes depending on how the fire develops.
The application of artificial intelligence thus presents opportunities to detect fire hazards at an early stage and to automate corresponding follow‑up processes such as alerting and initiating firefighting measures. AI‑supported systems can be used in both preventive and defensive fire protection.
The primary task of preventive fire protection is to prevent fires from starting and to limit their spread through structural, technical, and organizational measures. In fire protection, AI‑supported systems can evaluate sensor data, environmental variables, and historical events and can detect unusual heat patterns or smoke development through a network of highly sensitive sensors, thermal imaging, and monitoring cameras. Such systems can also evaluate air quality measuring devices and detect problems before they become perceptible to humans or conventional sensors. This means that overheated appliances, faulty electrical installations, or unusual smoke emissions can be identified automatically. Since these predictive systems detect fire hazards at an early stage and initiate preventive measures, they can facilitate a reaction even before the damage event occurs and thus make a significant contribution to increasing safety standards.
Possible areas of application for AI‑supported systems include the following:
In defensive fire protection, AI can significantly influence and optimize operational planning, firefighting, decision-making, and coordination during a fire incident. Examples of such optimization include the following:
AI‑based fire protection systems currently solve individual problems. Further developments are essential to consider additional structural, technical, operational, and organizational fire protection measures. In the future, improved sensor technology and data analysis are expected to increase the accuracy and reliability of AI‑based fire protection applications. In addition, robots that can be used autonomously in dangerous/inaccessible environments to fight fires will be used and AI‑based technology and systems will be integrated into a comprehensive safety strategy to ensure maximum safety (e.g., fire protection, intrusion protection, surveillance) and combat multiple potential threats.
The integration of AI into fire protection and property insurance marks a profound technological change with far-reaching implications for prevention, risk management, and customer experience. Until now, insurance exposure has been determined largely based on human intuition, experience, knowledge, historical data, actuarial tables, and numerous statistical analyses. It is a cumbersome and time-consuming manual process, characterized by risk assessment, reporting, valuation procedures, and premium pricing. It involves the inherent problem that the availability of historical data is limited by technological changes and environmental processes, and only allows limited predictions to be made about the loss burdens from catastrophic events.
Continuous collection, linking, and reconciliation of all available data in real time (e.g., loss data, IoT device feeds from the increasing number of networked equipment under the “Internet-of-Things” phenomenon, social media activities, financial documents, weather reports, satellite images) can identify individual, existing risk potentials and develop tailored coverage concepts. AI‑driven processes allow patterns and correlations of structured/unstructured data to be recognized to make a more dynamic and accurate risk exposure assessment and thus make more efficient / risk-adequate decisions. Furthermore, technological and environmental changes in risk assessment as well as coverage concepts and insurance premiums would be adapted to the individual needs of each policyholder and the existing situation. Additional advantages lie in the simulation/prediction of possible losses (e.g., catastrophe scenarios) for better individualization of the insurance premium and coverage concepts, and better traceability and rationalization of the decision for the policyholder. Forecasting capabilities and risk management practices for better strategic allocation of capital and resources can also be improved to minimize potential losses for insurers.
The central elements of property insurance are the determination of exposure and an adequate premium based on that determination with regard to the perils insured in the insurance contract, such as damage from fire, explosion, lightning, or water. Traditionally, this assessment is based on standardized risk models, historical loss statistics, and actuarial methods.
The application of AI opens up new possibilities for more precise, dynamic, and customized risk assessment. Compared to traditional methods, the large amount of process and claims data collected (e.g., telematics and sensor data) allows more complex patterns and nonlinear relationships to be considered. This feature enables the analysis of a wide range of additional risk indicators (including geo, environmental, and plant data) and their interactions, all of which can be analyzed automatically and simultaneously. The result is more accurate exposure models. These models can be updated continuously based on newly added risk data, such as changes in the system status of machines and plants, changes in usage, or changes in environmental data.
Consequently, it becomes possible to dynamically adjust the necessary insurance premium to the respective risk situation and the associated potential hazards. Concurrently, it becomes feasible to differentiate more distinctly between risks of the same type of operation but with different buildings, infrastructure, and safety structures. This approach enhances the precision of risk assessment. Risk modeling also facilitates the optimization of the allocation of underwriting and reinsurance capacity.
Alongside the advantages afforded by AI, however, are also a number of challenges: AI models and algorithms are generally very complex, and their results are difficult to comprehend. There are also regulatory, data protection, and ethical challenges, as personalized sensitive data can lead to discrimination in risk assessment. In addition, the risk models must be regularly reviewed and adapted to new risk situations.
The use of artificial intelligence to personalize insurance services In the property insurance sector, there is an increasing demand for policies that are customized to the specific risks and needs of individual policyholders, rather than relying on standardized coverage concepts. It is also essential for policies to be adapted more quickly and flexibly to changes in the risk situation.
AI technology offers the possibility of creating individual risk profiles by evaluating existing data (e.g., object and process data, geodata) and matching desired insurance services to an existing risk situation. This allows us to provide a customized and cost-effective insurance solution tailored to your specific needs and risk profile, ensuring you're not over- or underinsured. The following list contains some possible forms of design:
Although the use of artificial intelligence in fire protection and property insurance offers a wide range of advantageous opportunities, there are significant challenges associated with it. They can be summarized as follows:
The considerable benefits associated with the use of AI technology in property insurance and fire protection must be weighed against the associated technical and social challenges in terms of loss prevention, cost-effectiveness, and service quality. To ensure sustainable success, it will be necessary to link the responsible handling of data, transparent decision-making processes, and compliance with existing ethical and regulatory standards.
AI has the potential to fundamentally change the way fire protection and insurance is regarded and implemented. The use of AI in fire protection and insurance is still at a relatively early stage but will continue to develop; it will increasingly find its way into fire protection and property insurance. The future of AI promises a more dynamic, responsive, and personalized approach to risk management and assessment. Improved predictive analytics based on ever-growing data sets and real-time information, for example, will enable insurers to offer more efficient, data-driven, and proactive insurance cover.
The use of AI is subject to a wide range of legal requirements designed to ensure the safe, fair, and non-discriminatory use of data-driven technologies. In addition to existing data protection and insurance regulations, a regulatory framework specifically tailored to AI is becoming increasingly important with the European Union’s AI Act.1 Companies must therefore comply with both national and European Union standards to avoid legal risks and to create trust among customers and supervisory authorities.
Despite the potential offered by AI in property insurance and fire protection, its widespread implementation is associated with considerable challenges, notably relating to technological requirements as well as structural and cultural framework conditions within the organizations. Successful implementation therefore requires integrated management of these challenges that combines technical innovation with organizational readiness for transformation. At present, considerable financial and human resource investments in technology and the necessary infrastructure are required.
In the long term, it can be assumed that AI applications will not only enable selective improvements but will also lead to a fundamental structural change in the insurance industry and in preventive fire protection. The development of intelligent, autonomous systems, and the deeper integration of IoT, big data, and edge computing are paving the way for a data-driven, adaptive insurance industry.
All website links were last accessed on 11 July 2025.
