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Analytics in Insurance
   

Predictive Analytics: Accelerating Business Growth & Assessing Risk in Insurance Industry

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In the age of Artificial Intelligence and big data, the insurance industry strives to have the highest-quality data and top-tier analytics tools to transform data into actionable business insights. Predictive analytics, which is defined as the effective use of data, statistical algorithms, and machine learning methods to estimate future outcomes based on historical data, which is state-of-the-art in data analytics, is changing the ever-data-reliant insurance sector.

Though the growth of data creation is exponential in the insurance sector only a few have found a way to monetize the crucial asset ‘data’. As data becomes ubiquitous, it has become crucial to harness the power of data and analytics. But insurance executives often wonder, “where and how do we start?” Here is where our five-phase strategy to effectively manage analytics for insurance industry comes into play.


While more data, advanced tools, and new applications create opportunities in the insurance industry, predictive analytics points the way forward to help insurers develop distinctive go-to-market capabilities, give rise to new revenue streams, and capture significant rewards.

Thanks to remarkable technology advancements - customers can now purchase insurance at their fingertips through mobile applications. With predictive analytics and tech-driven shift in place, insurance companies can use different analytics tools to forecast customer behavior, future market trends, and insurance needs. What is predictive analytics all about and how it redefines the insurance sector are the key questions.

What is Predictive Analytics in Insurance?

This is a crucial area of analytics that focuses on making predictions about risks and possibilities of future events. As a result, it has grown to be an essential part of the insurance sector. Predictive analytics in insurance refers to various techniques, including data mining, artificial intelligence (AI), statistics, machine learning, predictive modeling, and more. Combining each of these techniques enables insurance companies to generate accurate reports, personalize marketing strategies, and target specific customer groups.

Deals with data analysis and interpretation to generate forecasts based on potential risks and events. With predictive analytics in insurance underwriting, insurers can now customize policy plans by tapping into granular customer details and understanding behavioral signals, price sensitivity, customer preferences, etc.

Although the use of analytics in the insurance industry is not new, it has grown significantly over time. In the past, insurers might have taken a few factors into account when calculating a policy's premium. However, organizations today are known to employ a dozen data points to calculate a premium that determines the cost of the insurance. We have covered a few aspects in detail considered by insurers while determining the premium of an insurance policy. Additionally, with the ever-increasing use of predictive analytics, it has become possible for insurers to assess risk and set rates to cater to the customized insurance needs of customers.

Unleashing the value of Predictive Analytics

Now that we understand what predictive analytics is, how insurers assess risk and set prices using it, and how it benefits both customers and insurers, let us move ahead to study how predictive analytics is going to shape the insurance industry:

Identifying Risk of Fraud: With insurance analytics in place, insurers can prevent potential fraudulent activity from happening in the first place. Predictive modeling is also used by insurers to spot fraud. A recent FBI study discloses that the annual losses related to insurance fraud are as high as $40 billion, resulting in increased premiums for an average American family each year. Advanced analytics brings in more opportunities to conduct fraud detection initiatives and combat them proactively.

Improve Claim Processing: Insurance clients are constantly looking for quick claim settlements. However, it can be difficult to process requests each time. Insurance companies have the opportunity to prioritize some claims over others in order to save time, money, and resources by optimizing predictive analytics. Additionally, it increases the likelihood of repeat business and consumer satisfaction. For instance, a UK-based company reduced the number of accidents by integrating telematics and tachograph infringement data with weather data and leveraging the data set with ML. Using advanced analytics helped the company improve driver safety and reduce insurance payout.

Behavioral Analysis: Analyzing and predicting customer behavior is one of the most common uses of predictive analytics that can save insurance companies from unexpected scenarios. Behavior becomes a little less erratic with the use of predictive analytics since you can determine what causes a response based on historical customer data. The ability to identify those who are more receptive to persuasion than others is another advantage of the uplift modeling technique for behavior prediction.

Data Management & Modelling: Predictive analytics' ability to function properly is dependent on a number of factors, including how data is handled and modeled. Data architectures are required by insurers to simplify, augment, and transform the data landscape and leverage existing data efficiently. With effective data management in place, insurers can build robust customer profiles and create new opportunities to cross & upsell insurance plans. Data modeling enables insurers to use information extracted from data management platforms to offer on-demand services to their customers.

How Technologies Help Determine Insurance Premium and Assess Risk

Insurance premiums equate to the rates insured parties pay to cover a possible unseen loss. Actuaries are employed by insurance companies to assess the risk associated with insurance plans that cover life, property, liability, and other types of insurance. The insurance premium paid by the insured party is based on the amount of risk that the insurance company must take on.

Actuaries use risk analysis software and apply statistical and mathematical models to demographic information about the insured person as well as a range of data from other sources to determine the possibility of death, disability, illness, injury, or property loss. Insurance organizations invest premiums based on actuaries' recommendations so they are sure to have enough money to cover any potential claims.

The explosion in existing customer data, the rapid growth in advanced analytics technologies & tools, and the quickly declining cost of data storage and computing power propel companies to invest in data analytics as a means to innovate and succeed. As Stewart Brand once quoted, ‘Once a new technology rolls over you, if you're not part of the steamroller, you're part of the road.’ Business players who rethink their approach and consider data-driven decision-making are likely to rule the business world.


A Framework for Success

While more data, advanced analytics tools, and new applications are creating new opportunities and possibilities in the insurance segment, to thrive in this competitive world of advanced analytics, insurers need to effectively manage large-scale organizational transformation. A viable framework can lead to success in insurance analytics.


Conclusion

Organizations that integrate predictive analytics into their insurance businesses are likely to increase their market share create and value proposition. When data is put to the right use and can determine strategic movement for an insurance company, organizations will be able to customer loyalty and grow their revenue significantly while reducing costs & time. At Polestar Solutions, we help insurance companies achieve success on top of their data and derive maximum business value with agility & flexibility.

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