Earnix highlights the potential of synthetic data in specialty insurance

Earnix, a leader in AI-based SaaS solutions for financial services, has released a white paper highlighting the transformative potential of synthetic data to accelerate profitable growth and risk understanding in the context of the unique specialty London insurance market.

The white paper Reshaping London’s Specialty Market -The Impact of Technology and Data highlights the pivotal role that synthetic data – data that is produced, rather than gathered – will play in the future success of the market.

While synthetic data has been utilised in academia for decades, it’s only recently gained traction in mainstream commercial applications. According to Gartner, synthetic data accounted for just 1% of data in 2021 but is projected to grow to 60% by 2024. Technological advancements like generational adversarial networks, transformers, and large language models have significantly increased the speed and volume of synthetic data created.

At the same time, the increasing demand for data in specialty insurance has prompted underwriters and brokers to begin harnessing synthetic data to supplement existing datasets, improving risk assessment efficiency. This is because synthetic data offers solutions to several key challenges in the London Market:

1. Filling Data Gaps: Specialty insurers can harness synthetic data to access necessary data for product development and testing without compromising security or customer privacy. This has led to a surge in innovative ideas and technology deployment in insurance.
2. Privacy and Security: Synthetic data maintains confidentiality while enabling critical analysis. In a world of widespread data availability, synthetic data techniques are essential to ensure customer data is not vulnerable to identity theft or fraud.
3. Analytical Support: Synthetic data supports more accurate modeling, simulation, and testing, particularly in assessing extreme or rare events crucial for the specialty market, such as floods or hurricanes and fraud detection. Insurers can simulate these events to develop robust risk models without waiting for actual occurrences.
4. Simulations, Stress Testing, and Data Expansion: Generating synthetic data improves the performance of predictive models, enhancing fraud detection and other analytical insights. It allows insurers to simulate and stress-test models in new situations, enabling them to introduce new products or expand into new segments or territories with limited existing data.

Comments from Earnix CEO Robin Gilthorpe

“The digital transformation of the London market’s complex specialty lines of business has only just begun. While it is a market-wide and fundamental shift in the way specialty insurers and reinsurers do business, it is not a one-size-fits-all solution. However, synthetic data coupled with AI, machine learning, and advanced analytics, holds promise for addressing various challenges in specialty insurance, including data gaps, privacy and security concerns, and fraud detection, among others.

Data plays a crucial role in revenue, profitability, and market share- particularly in this complex market where insight at a granular level can make a significant difference at a macro level. Specialty brokers and insurers know that in this rapidly evolving market, the ability to grow through change is paramount. Our white paper underscores the pivotal role of synthetic data in reshaping the London Market and outlines the next steps available to specialty insurers to harness the unparalleled opportunities for informed decision-making and enhanced operational efficiency.”

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• InsurTech