AEGIS selects expert.ai to boost its data strategy
AEGIS has chosen the expert.ai natural language platform to implement a risk engineering solution that will extract and analyse critical information from risk engineering reports during the property underwriting process.
By applying the power of AI to this information-intensive process, AEGIS engineering teams will collect data more efficiently and comprehensively, enabling them to provide deeper insights to both underwriters and policyholders regarding key risk exposures and mitigation strategies.
“Artificial intelligence will continue to play a crucial role within AEGIS’s digital transformation program which aims to maximize our efficiency and increase our data analytics capabilities,” said Scott Schenker, SVP and Chief Information Officer at AEGIS.
“We’ve selected expert.ai for its ability toread, organise and extract relevant data so our team can be more efficient both in terms of managing repetitive tasks as well as better serving our policyholders.”
Tim Heinze, SVP and Chief Loss Control Officer at AEGIS, said in the course of evaluating and selecting an AI platform, expert.ai outperformed all of its evaluation criteria in terms of ensuring speed and efficiency.
It also delivered high-quality information needed to properly assess the risks of its policyholders and provide domain expertise based on the data collected.
By automating the reading and extraction of relevant data from natural language texts, expert.ai offers a range of AI services to reduce risk, improve risk selection and pricing, and augment capacity for insurance carriers and brokers.
"We help insurers understand and extract the data they need from loss control and property reports, policies, renewals and submissions, at scale and more than four times faster than their standard," said Michael Watt, Vice President of Insurance at expert.ai.
"All of this is performed with maximum accuracy, ensured by expert.ai’s unique approach to mixing language understanding, a robust knowledge graph and machine learning."
