Big data, data analytics, predictive analytics. Whatever the term, the results rely on data, but not just any data. Quality data is needed for quality results. Unfortunately, many insurance companies struggle to deliver quality data in the quantities required for reliable data analytics insights. To deliver useful insights, insurers need to overcome obstacles such as access to the data, quality of data, and organization silos.
About 55% of a company’s data is inaccessible because it is unstructured. Unstructured data includes emails, documents, videos, presentations, or any data stored in a consistent format. Structured data consists of spreadsheets, databases, and files with a specific and uniform format.
Computers love structured data, and most data processing tools were designed for structured data. Today’s data comes in varying formats, making it difficult to integrate data for analysis. In fact, data scientists claim that 80% of a data analysis project comes from acquiring, cleaning, and preparing data.
Crucial to quality analytics is quality data. If data is missing or invalid, the results are faulty. That’s why cleaning the data is the first step in developing reliable insights. Data quality issues include the following:
Once data has been cleaned, it needs to be converted or transformed into a common usable format. Having a standard format makes it easier for algorithms to produce actionable insights.
According to a 2019 study, predictive analytic tools have helped property and casualty insurance companies improve profitability and customer satisfaction. These tools can be used to integrate data from internal and external sources to predict consumer behavior. They are used for the predictive modeling of multiple scenarios to determine the most favorable outcomes.
Integrating external and internal data into a predictive model can help identify customers who may be unhappy with their coverage or their insurance company. Knowing what to look for when evaluating customer behavior can help insurers address issues before they become reasons to defect to another carrier. With advance notice, companies can provide the needed attention to retaining customers.
A 2020 report found that property and casualty fraud costs insurers about $30 billion per year. Predictive modeling can find discrepancies in the information provided by the insured party and third-party providers. They can look at social media and online activity to determine the likelihood that a third party or the insured would file a fraudulent claim.
Claims management can make or break a customer experience. Consumers want fast processing of claims with a personal touch. Making that happen can be difficult without the insights of data analytics. With the right modeling, carriers can look at claims and prioritize them to maximize resources to deliver a better customer experience.
Identifying claims that may result in high-cost losses early can save insurers time and resources. With analytics tools, companies can look through volumes of data to determine what characteristics are most likely to result in high-cost losses. They can use that information to alert personnel to the potential risk.
Data analytics can improve internal processes. Based on historical data, teams can identify areas for improvement and even look at what-if scenarios to see what impact changes would make in the overall process. It’s another way that data analytics insights can improve operations.
For data analytics to work, insurers need technical resources that can make the inaccessible data available. They need to find ways to deliver quality data in a consistent format to ensure accurate results. OZ offers a range of Analytics and Artificial Intelligence (AI) solutions that provide insights into every aspect of your operations. If you’re looking to disrupt operations on your way to digital transformation, schedule an appointment to begin the process.
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