Data Analytics

Although the life insurance industry has been cautious by nature, data and technology are shifting the analysis of risk and enabling prudent underwriting without volatility.

Employee benefits carriers can differentiate themselves and shield themselves from disruption by harnessing predictive models to optimize pricing and radically improve profitability.

Off-the-shelf technology packages rarely produce exactly what an organization needs; with low-code, they can configure solutions to their unique needs.

California warns that it is watching and that "bias and discrimination in any form will be investigated and will not be tolerated."

Using a logical data fabric, insurance companies can apply a modern, agile approach that also allows them to streamline their data governance efforts.

Demand for data in today’s insurance companies is outpacing supply, and, in most cases, the throttle is organizational rather than technical.

Insurance must get beyond the hype of the "I" in AI and become more pragmatic in its use of AI/ML technologies for generating business insights.

Integrating a new feature, for example to provide dynamic quoting and pricing, usually takes months. Hyperautomation, such as low-code, provides robust solutions faster.