Contemporary challenges, such as the multiplication of digital threats and climate upheavals, impose radical adaptations on insurers. Data governance proves often perilous under the ever-increasing demands of the GDPR and national regulations. *The integration of synthetic data thus becomes a crucial lever for innovation*. The latter, designed to imitate the characteristics of real data without compromising confidentiality, represents a necessary strategic shift. *The fight against fraud and the anticipation of climate risks require unprecedented agility*. Insurance companies thus have a powerful tool to shape their future.
The current issues faced by insurers in the face of digital threats
The insurance sector is confronted with an explosion of digital threats and an increase in climate risks. Companies are seeking to apprehend these challenges through the integration of Artificial Intelligence, a tool that has become essential for identifying risks and combating fraud.
However, the regulatory framework, notably the GDPR and the EU AI Act, as well as the actions of the ACPR in France, complicate access to real data. In this context, synthetic data emerges as an innovative solution.
Synthetic data: a response to data governance
Insurers possess a variety of data about their clients and the associated risks. However, they must face challenges related to governance and the accessibility of this data. Despite massive investments in modernizing information systems, many are still using legacy IT infrastructures, limiting information flow.
Current regulations impose restrictions, particularly on the use of sensitive data. Synthetic data, generated through statistical models and machine learning, offers a solution by reproducing the characteristics of real data without disclosing confidential information.
Improvement of fraud detection through synthetic data
Insurance fraud is proving to be a growing problem, with nearly 695 million euros identified in 2023. This plague represents about 10% of the compensations paid each year in France. Insurance companies aim to enhance their real-time detection capabilities.
Synthetic data allows the training of AI models with a variety of scenarios, whether real or fictitious. This approach helps insurers to simulate numerous claims, thereby facilitating the identification of abnormal patterns and the assessment of risk scores.
The challenges of new climate risks
In the face of recent extreme weather phenomena, such as droughts and floods, a redefinition of risk assessment methods is necessary. According to the UN, these climate events have doubled in two decades. Historical data is no longer sufficient to anticipate these new threats.
Government reports, such as the one by Langreney, encourage data sharing among insurers, but its implementation remains complex. Synthetic data appears as a viable alternative, allowing for the simulation of extreme events in areas previously little exposed, facilitating the impact modeling on future insurer commitments.
Precautions and excellence in the use of synthetic data
The use of synthetic data requires precautions to avoid reproducing biases present in the initial data. The adage “garbage in, garbage out” reminds us that the quality of input data determines the reliability of results. Projects must be structured around an approach that includes value, governance, and technology.
A precise identification of use cases, such as fraud prevention or climate modeling, is necessary. The balance between compliance, ethics, and performance must also be considered. The accessibility of synthetic data generation platforms aims to promote their adoption across all business functions, whether related to underwriting or claims management.
Insurers, while taking into account new requirements regarding transparency and ethics, must adapt to market evolutions. The implementation of AI-based solutions and synthetic data is now a strategic lever for the resilience and competitiveness of companies in the sector.
Frequently asked questions about synthetic data in the insurance sector
What are synthetic data and how are they generated?
Synthetic data are artificially created data derived from statistical models and Machine Learning algorithms. They faithfully reproduce the characteristics of real data without revealing sensitive information.
What advantages do synthetic data offer to insurers?
Synthetic data allow insurers to better understand risks, enhance fraud detection, and simulate various scenarios to anticipate extreme climate events.
How do synthetic data help comply with data protection regulations?
Synthetic data allow for circumventing certain constraints of the GDPR or the requirements of the ACPR, as they do not contain identifiable personal information, thus facilitating their use in various contexts.
Are synthetic data reliable for predicting real behaviors?
Yes, as long as they are generated from high-quality real data. However, it is essential to avoid biases in the source data to ensure that synthetic data remain representative.
What is the role of AI in the use of synthetic data by insurers?
AI is used to create advanced models that generate synthetic data, as well as to analyze this data to enhance fraud detection and risk assessment.
How can synthetic data improve climate risk management in insurance?
They complement historical data by simulating extreme scenarios in geographical areas that have been little exposed, enabling insurers to better model the impact of climate changes on their commitments.
What impact will synthetic data have on the future of the insurance sector?
Synthetic data are seen as an essential strategic lever for resilience, competitiveness, and survival of the sector, facilitating regulatory compliance and optimizing decision-making.
How can companies integrate synthetic data into their processes?
Companies should adopt a structured approach, identifying specific use cases, balancing compliance and performance, and making these data accessible to all relevant departments.
