Today customers have the option of buying insurance on their phone through apps, thanks to the remarkable improvements in technology. In the same vein, insurance companies are being equipped with capabilities that allow them to have foresight about the future through predictive analytics. So, the fundamental question here is – what it is all about and how it is reshaping the insurance industry.



What is Predictive Analytics in Insurance?

This is a branch of analytics that is concerned with making predictions on the risks and probabilities of future events. Hence it has become an integral component of the insurance industry. The term “predictive analytics” in insurance encompasses a variety of methods including – data mining, statistics, machine learning, AI, predictive modelling, and so on. All these methods in combination allow insurance companies to generate reliable reports to precisely identify the level of risk aiding underwriting and policymaking.

The usage of predictive or insurance analytics is not new to insurance, but today it has become a whole lot complex than in the past. Earlier, insurers might have considered a few variables to determine the premium for a policy. However, today companies are known to use a dozen of data points to arrive at a premium determining the pricing of the policy. Also, the scope for offering personalized policies to customers has become possible with the increased usage of predictive analytics.

11 ways Predictive Analytics is Going to Shape Insurance Industry

Now that we understand predictive analysis or analytics, let us turn our attention to how predictive analytics is going to shape the insurance industry. Here we go:

Pricing and Risk Selection: This is not new to insurance, but the way data is going to be collected and analysed makes it better than the way it was previously done. Previously there was a lot of dependence on demographics material like credit history and criminal record of a person. These in most cases were not entirely accurate. In contrast, today data is getting harvested directly from first-hand data sources like smart devices and conversations between claim specialists and the customer. Such a sea change in data collection is instrumental in showcasing better data insights for Pricing and Risk Selection.

Identifying Customers at Risk of Cancellation: Predictive analytics can play a critical role towards customers who require unique attention. These are customers who are more likely to cancel or opt for lower coverage. Advanced data insights can shed light on identifying such customers who may be unhappy with their coverage or the insurance company. Such knowledge of customer behaviour will allow companies to offer personalized attention and alleviate potential points of friction. Without the comfort of predictive analytics, insurers would miss early warning signs and lose valuable time that could be used to bring the customer back to the fold.

Identifying Risk of Fraud: There is close to $80 billion lost annually in fraudulent claims in the United States alone. This makes up 5-10% of claims costs of insurers in the US and Canada. With the help of predictive analytics, insurers can pre-empt potential fraud from occurring in the first place or can retroactively pursue corrective measures. Insurers also depend on predictive modelling to detect fraud. With the help of big data and modelling, the insurer can identify red flags from the association between the insured party and third parties involved in the claim. In addition, data from social media is also being fed into predictive models to unravel suspicious activity after the claims have been settled.

Triaging Claims: Insurance customers are always looking at claims settlements to be done swiftly. However, getting it done every time poses a challenge. With the use of predictive analytics, insurers are equipped with the capability to prioritizing certain claims over others to save time, money, and resources. This also helps improve the chance of repeat customers and increased customer satisfaction. This notwithstanding, predictive analytics can anticipate the insured’s needs, remedy their concerns which improve their relationship with the insurance company.

Enhanced Customer Loyalty: Predictive analytics provide the insurers the luxury of observing the history and behaviour of loyal customers and can even anticipate their needs. Such an impeccable level of service will only encourage the customer to remain with the insurer for a long time to come. In today’s day and age when brand loyalty is becoming rarer, such practices will allow an insurer to retain customers preventing them from getting enticed by the competitors.

Identifying Outlier Claims: Predictive analytics can identify claims beforehand that can unexpectedly turn out to be high-cost losses, also known as outlier claims. By studying previous history and understanding patterns with predictive analytics, insurers can send alerts to claim specialists. With prior notice on such claims, insurers can reduce the complications and potential losses arising out of them and even cut down their number by a certain margin through pre-emptive steps with the help of predictive analytics.

Transforming the Claim Process: With the use of predictive analytics, insurers can use data to determine events, information, and other factors that influence the outcome of claims. This will allow the streamlining of the process to the shortest possible time without prolonging it for weeks or months. Besides this, by analysing historical data about claims processing insurers are empowered to make informed decisions that enhance the overall efficiency.

Data Management & Modelling: One of the conditions for the smooth running of predictive analytics pertains to the way data is managed and modelled. When data is scattered across disparate systems and when there is no plan for utilizing it – the potential value of the data is wasted. It is only when data management solutions are implemented that predictive analytics would be able to build robust customer profiles and create opportunities to cross and upsell insurance policies. This apart, through data modelling insurers would be equipped to deliver on-demand services to their customers via the cloud using insights extracted from data management platforms.

Identifying Potential Markets: Predictive analytics can be deployed for identifying and targeting potential customers. In fact, data can reveal behavioural patterns and specific characteristics among demographics giving insurers a clear understanding of who should be targeted. There are more than $3.2 billion people on social media, and the data generated from these platforms have become vital for identifying new markets.

360-degree View of Customers: The term “360-degree view” refers to a comprehensive view of the customer through the aggregation of data from different touchpoints that a customer utilizes to contact a company, purchase products, receive service, and customer support. With the use of predictive analytics, the insurers can readily consolidate data and generate new insights to paint an elaborate picture of the customer. This analysed data would reveal insight about their risk profile, probability of expanding coverage, an overview of their buying habits, and so on. Before the active use of predictive analytics, insurers would often stick with guesswork and gut instincts to arrive upon such decisions that are no longer the case.

Providing a Personalized Experience: There are many customers in the market who actually like a customized experience when it comes to buying insurance. Predictive analytics can sift through IoT-enabled data to understand needs, desires, and advice for customers. In the future, there is likely to be increased use of predictive analytics by insurers to forecast events and have actionable insights into all aspects of the business. Such insights would be integral to establishing a competitive edge in the market with added savings on time, money, and resources.

Conclusion

While the blog may have enlisted the trends associated with predictive analytics or predictive analysis in 2021, the scope for predictive analytics is much wider in the insurance industry. In the coming years, we are likely to see established & upcoming companies and new entrants in the market use predictive analytics in a big way to effectively plan the future course of their business. Overall, there needs to be a strong belief in the strategic value of data. And the understanding that when data is put to the right use generates actionable insights which can determine the strategic movement forward for an insurance company.

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