Predictive analytics in the insurance industry - Saxon AI
Insurance / May, 04 2023

Crunching the numbers – Predictive analytics in the insurance industry 

According to a recent survey, the global insurance analytics market is snowballing at 15.1% CAGR and will reach USD 20.6 billion by 2026. 

Insurers deal with immense volumes of data in their businesses. That data is like gold, adding vigor to their long-term goals by augmenting good decision-making. Insurance firms use data for various aspects, such as overcoming business challenges, competitive business growth, digital transformation, and automation of processes. More so, when the data is aptly managed, it leads to making data-driven favorable business decisions. Predictive analytics play a key role here. It is a combination of ML algorithms and AI tech (such as neural networks) which gathers and analyses large volumes of past and current data, identifies patterns and trends, and makes accurate predictions. These predictions not only help in fraud detection and claims processing but overall optimizes and accelerate the insurance business in many ways. Let us dive deeper into the importance of predictive analytics in insurance and explore some use cases and benefits in this blog. 

Use cases of predictive analytics in insurance

Let us look at the various ways you can apply predictive analytics in insurance. 

Pricing and policy optimization 

Customers favor insurers that can offer tailored policy options according to their requirements and preferences. Rather than making people fit into your fixed coverage plans based on their age or other variables, you can use predictive analytics to build flexible policies that vary based on customer preferences, claims history, and behavioral indications. 

Assessing the likelihood of a policyholder getting involved in a car accident to decide the insurance premium is one of the most familiar examples of using predictive data to optimize a policy. Predictive analytics considers other external factors like market circumstances and associated risks in addition to customer behavior data. The combination of machine learning-enriched AI pricing enables insurers to provide lower premiums, safer drivers, and enhanced customer experiences. Not just that, it instills motivation in the drivers to drive safely to improve the conditions of their policies. 

Fraud detection 

Every insurance company’s biggest challenge is detecting and preventing fraud. Using past data from the claimant’s activities, you can use predictive analytics to spot possible fraud. AI-driven systems can detect potential fraud before its occurrence by scrutinizing online activities and spotting discrepancies between the claimant’s behavior and the third parties involved in the claim (like hospitals, repair shops, etc.). In addition, insurance companies can also combat internal fraud and application manipulation using big data analytics. 

Insurance Claims Management

Predictive analytics can help insurers automate claims processing by using algorithms to analyze claims data and decide claim validity, eligibility, and payment. This can streamline and speed up the claims process and reduce errors. Advanced predictive analytics solutions can manage the end-to-end FNOL (First Notice of Loss) process, which includes claims triage, more swift and agile. Additionally, these predictive analytics platforms are easy to integrate with legacy or existing software, further supporting your efforts to go digital. 

Predictive analytics can also help insurers find ‘Outlier Claims,’ which suddenly become high-cost losses. Examining past claims for similarities sends automatic alert claims to claim managers. In this context, early reporting can assist insurers in reducing these irrational claims. 

Dynamic customer engagement

Insurance firms can better understand their customers’ evolving behaviors and requirements by utilizing AI. They can engineer dynamic customer journeys that change in response to advanced customer analytics. Some examples of using predictive analytics to provide agile customer engagement are as follows. 

  • Having ‘smart bots’ or AI-powered chatbots for insurance 
  • Deciding on the preferred agent for a specific customer using ‘predictive routing.’ 
  • Customizing communication approach following the consumer data. (For example- sending tailored offers to reassure at-risk customers.) 

Customer risk prediction

Insurance companies can segment consumers based on their risk profiles using AI-based predictive analytics tools. Health insurers, for instance, use predictive analytics to evaluate the mortality and morbidity risks associated with each client and tailor their policies accordingly.  

Enhancing the insurance underwriting process 

You can accelerate the insurance underwriting process using data models built on predictive analysis. This also enables insurers to identify risks associated with insuring individuals or assets. Furthermore, predictive data enables underwriters to complete duties more quickly. According to most respondents, using technology has improved their ability to manage more businesses and given them access to more expertise. It has also sped up their ability to quote. 

Key benefits of predictive analytics in insurance 

Let us discuss the key benefits of predictive analytics for insurance businesses here. 

Customer relationship management 

Data insights can detect dissatisfied policyholders on the verge of churn by analyzing customer behavior patterns. Armed with this information, insurers can focus on nurturing these disgruntled customers and attempt to enhance their experiences.  

Tech innovation 

Predictive analytics removes repetitive tasks and decreases underwriting time, reducing headcount costs. Moreover, it also increases fraud detection rates and mitigates fraud- relates losses, thereby significantly reducing expenses. 67% of insurers claim that predictive analytics has helped cut down expenses. 

Higher revenue growth over competitors 

Competition is an excellent incentive for businesses. Predictive analytics gives a competitive edge to insurers by boosting revenue growth and staying a step ahead of their rival firms. With the capabilities of offering reasonable plans, faster claims processing, and personalized CX, it helps retain present customers, attracts new ones, and builds up a competitive advantage. 

Tailored customer experience 

Customers always love the tailored experience. Using predictive analytics, you can anticipate your customers’ specific requirements and behaviors and tailor personalized interactions for them. For example, you can apply it to provide insurance policies that are tailored to the customer’s claims histories. 

Helps in identifying potential markets 

Predictive data analytics makes it simpler to expand into existing and new markets. To uncover potential markets before your competitors and broaden your reach, you can utilize predictive analytics to show your audience’s common traits and patterns of behavior. Predictive analytics can uncover your customers’ behavior patterns and traits, using which you can uncover potential markets before your competitors and broaden your reach.  

Automate processes and drive data-driven decisions 

AI can help insurance agents deal with data much better- from data-driven decision-making to identifying the processes they can automate. For instance, by detecting fraud and reducing underwriters’ workload, AI solutions give insurers the time to concentrate on informed decisions. 

How can we help? 

Predictive analytics has a significant positive impact on the insurance industry- with good potential to improve market share and accelerate growth. Enterprises embracing this tech are likely to thrive in this highly competitive market. If you are seeking a tech partner to assist you with the mentioned requirements, Saxon is an excellent choice. We assist enterprises in harnessing data and help accelerate their businesses.  

Please have a look at our solutions to find out more!

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