Five key ways to help stabilize the individual and small group health insurance markets in 2018

whitePaper | May 23, 2022

Insurance Post, in association with LexisNexis Risk Solutions, gathered experts together to discuss how issues such as ghost broking, the current limitations of benchmarking and fraud detection are pertinent to rooting out motor insurance fraud. This content provides a summary of the discussion.

whitePaper | January 12, 2022

Insurers need to tackle the rapid changes to their ecosystem. They instinctively react in the same way they have always reacted in previous crises — by making marginal adaptations to the traditional way of doing things and further optimizing and streamlining the existing business model.

whitePaper | January 14, 2020

After months of strategy and budget meetings, you’ve got the green light – it’s time to begin building your company’s quality assurance program. But are you prepared for the realities of the creation process? Can you address the inevitable questions and setbacks as they appear?

whitePaper | October 27, 2022

A recent survey by Insurance Post found that 98.1% (see figure one) of respondents believe the current economic conditions in 2022 will impact organised and opportunistic fraud volumes.

whitePaper | July 3, 2022

The D&O insurance market over the past decade experienced rate reductions, broadening coverage and increasing claims frequency and severity. This meant that losses outpaced premiums and the industry needed to respond. The magnitude and speed of the response that followed surprised many market participants. Significant capacity shortage led to sharp price increases and in many cases to reduced limits purchased by companies. The outlook for the D&O market remains challenging as the risk environment is more uncertain than ever.

whitePaper | July 19, 2022

ML, a branch of artificial intelligence (AI), operates under the hood in many digital services we use today – including search engines, social media, e-commerce, and voice assistants. Automated, ML-powered systems have virtually unlimited computing power, resources, and memory in comparison to human counterparts and can be taught to “connect the dots” during data analysis.