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Battling the bottleneck: The significance of information preparation in insurance coverage

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However, some insurers are stuck in the past and use manual processes like Excel spreadsheets and other text or code driven platforms to manage data for risk insights and claims analysis. These old-fashioned data wrangling programs are not equipped to handle the size or complexity of the data that insurers can access today.

According to executives at Trifacta, a data preparation SaaS introduced by some of the largest insurance companies in the world, when insurers rely on outdated data management tools and strategies, this has a negative impact on their performance. In order to improve your work processes, a suitable data preparation tool is of crucial importance.

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“We are now approaching a situation where the ecosystem has evolved so that people are trying to do more with less. While that is always the goal, insurers are now doing it in what we would call the modern stack ecosystem, including cloud-based platforms, ”said Chris Moore (pictured), director of North American sales engineering and solutions at Trifacta .

Many property and casualty insurers (P&C) now use weather data to better calculate and predict where they should cover property risk. These weather datasets are new, complex, and available in very large quantities, which require sophisticated data preparation and wrangling tools.

“The tools that insurers have used over the past 20 years are generally not good at keeping up with this volume,” said Moore, “and they are often more limited in allowing people to understand the data , that you have.” We work with it and shape it into the insights you want on a scale. “

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Data preparation is the key across the entire insurance value chain. For example, with the right claims analysis tools, insurers can accelerate common tasks required for predictive modeling or loss forecasting and transform them into more automated, more predictable pipeline workflows. The insights gained through data wrangling can also help uncover patterns that are helpful in determining risk appetite, desired market capacity, and even insurance fraud.

“It has become common knowledge that in the insurance industry, and in any data-intensive industry, almost always where the data is being prepared is where the real heavy lifting happens,” Moore told Insurance Business. “A tremendous amount of time is spent just preparing, cleaning, and prepping data so that it can be used by the analytics teams, model development teams, and other value-added activities in the organization. We are excited to be the maintainers of these people because we know that a lot of time is spent there before these valuable insights can ever actually be gained.

“Insurers have access to such a diverse range of data sets, and the talent to process all of that data is spread across the board. Not everyone has the amount of money to hire all of the quants and data scientists in the world, and at the same time the talent they have must be qualified to learn how to prepare data with new tools on new platforms and with new datasets they still have have never seen. We’re here to help you make this challenge easier without everyone having to be a skilled data scientist. ”

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