Data science

Dr. Bradley Heltemes, VP and medical director of R&D at Munich Re Life US, and Dr. Tim Meagher, Vice President and Medical Director Munich Re, Canada (Life) discuss how AI and technology are playing a more prominent role in the life insurance sector and improving cancer patient outcomes.

Jamie Warner of Plymouth Rock Assurance focuses on the use of data and technology in the insurance space, and the STEM job opportunities available for today's graduates.

Earlier this week, NOAA scientists and staff were told to log and clear all international contacts and communication, according to internal communications seen by Bloomberg News.

Exploration of the root causes of the workforce gap and potential solutions to bridge it.

Jodie Wallis, global chief analytics officer at Manulife, which operates as John Hancock in the U.S., spoke with Digital Insurance about how the company is deploying GenAI.

The power to quickly ingest and analyze large loads of data has become more critical to many businesses that require quick turnarounds in their decision making, technology leaders said.

Digital Insurance spoke with three actuaries about emerging technologies and the challenge of explainability within complex models.

The senior data scientist shares about his work in identifying demographic bias in machine learning models.

Data scientist Cathy O'Neil warns that insurance companies need more risk pooling.

Issues behind insurers developing their own software or deciding to purchase an external solution and the pitfalls of both paths.

Insurer chose data analytics and management company to fill data operations gaps

Success depends on people, culture and processes.

The company is looking for people with skills in software engineering, data science and cybersecurity.

MetLife has hired Carolina Stein, who will be based in Buenos Aires, Argentina.

Sears Merritt will lead technology-focused strategies to accelerate digital transformation at the company.

Some consumers don’t seem to be crying out for usage-based insurance and evidence shows some of the models in use are only mildly predictive at best. Why is that? Why is it that auto insurance companies have not found a way to significantly improve on the traditional metrics of age, zip code and the like despite all the technological advances of the last few decades?

Inside the near-term tech plans for the multiline insurer.

Ppooling the same type of transactional data from multiple insurers can help paint a much clearer picture.

Toronto-based company recently raised $3.5 million in seed funding for commercial insurance platform.