13 December 2022Analysis

BMA survey shows lag in takeup of AI/ML by Bermuda commercial insurers

More than half of insurance groups and the broader commercial market plan on using artificial intelligence/machine learning (AI/ML) within the next 5 years, according to a report by the Bermuda Monetary Authority (BMA).

The BMA) conducted a market-wide survey with commercial insurers and insurance groups to gather information about the sector’s use of artificial intelligence (AI) and machine learning (ML) technologies in their respective operations. The survey was also extended to small commercials, innovative insurers and intermediaries, which usually employ a digital-first approach in their operations, frequently utilising AI/ML systems.

Varying responses were received regarding whether companies currently use AI and ML technologies. For example, insurance groups reported that 68% are using AI/ML systems; this outcome is expected due to the scale of business operating internationally, the BMA said. However, the opposite is true for small commercial insurers, with just 18% saying that they used it. Furthermore, over half of the large commercial insurers (58%) do not currently use AI/ML technologies.

Among the 62% of respondents that do not use AI/ML technologies, 23% indicated that they plan to adopt AI/ML in the next five years or less. These survey results suggest that 54% of insurance groups and the broader commercial market plan on using AI/ML within the next five years, allowing the Authority and the Bermuda insurance market sufficient time to develop a creditable and fit-forpurpose AI/ML framework.

According to the BMA the top challenges and obstacles preventing the adoption and usage of AI/ML systems include:

  • AI/ML systems not being critical to current business offerings
  • Presently, no viable business case
  • Lack of requisite skills and expertise to implement these technologies
  • Limited budget

When asked about the areas of concern insurers have when considering adopting AI/ML systems, the top responses were explainability, auditability, modelling challenges, system security, transparency and consistency of outputs.