It would not be an understatement to say that the insurance industry is built on data. For decades, insurers have harnessed data and analytics to drive risk analysis decisions, and perhaps no other segment has done that better than personal lines. In particular, data has transformed underwriting, and new research paints a picture of how insurers plan to continue leveraging data's power to accelerate underwriting transformation.
SMA's new eBook, "Personal Lines Underwriting Transformation: The New Age of Data," shows that 96% of insurer executives see personal lines underwriting undergoing significant changes within five years – remarkable given the shifts that have already occurred in the segment. Data will drive much of the transformation ahead, and insurers are being strategic about where they focus time and investments. According to the eBook, 76% of insurers are implementing data pre-fill, and 64% are in the same stage with data/analytics scoring. Nine in ten insurers also have active plans in data augmentation.
Transformational technologies will also be critical in advancing personal lines underwriting, with 81% of insurers saying they are pushing transformation forward. Digital data generated by the Internet of Things, telematics/autonomous vehicles, aerial imagery, wearables, and other sources can produce new insights into risks. As a result, insurers can achieve better precision in risk evaluation and pricing, particularly when accessing these new data sources with the help of AI.
Data will continue to be the cornerstone of transformation and remains mandatory to pursue change within personal lines underwriting. But the focus should not be on one area – insurers must balance multiple initiatives and solutions that work harmoniously together to successfully move their underwriting departments into a new age.
Learn more about the current state of underwriting transformation and the path forward for the personal lines segment by reading SMA's new eBook, "Personal Lines Underwriting Transformation: The New Age of Data."
This blog entry has been reposted with permission from SMA.
