What Is Predictive Analytics in Insurance and How to Use It

The technological landscape changes, and so the industries do. Even though the businesses are cautious of trying costly automation tools, there are some enhancements warmly welcomed by everybody in the sphere. And the improved data management under predictive analytics is one of them.

When it comes to the limits of predictive analytics in insurance, the main concern is on the extent to which the sphere can apply artificial intelligence in its daily operations. Throughout the years, insurance was not eager to introduce new technologies.

However, some technical improvements have shown their benefits, especially car sensors, telematics, and social media software. Thanks to these know-hows, it’s possible to discuss the role and application of predictive analytics in the insurance industry today.

Predictive Analytics in Insurance Use Cases

What Is Predictive Analytics?

In essence, predictive analytics is an analytical tool that keeps track of historical records. By searching for connection and logic in these data sets, it forecasts upcoming events.

In practice, applying predictive analytics aims to reach the entire business's higher profitability and the effectiveness of its common practices.

Using and drawing models from the set of data, algorithms, and machine learning practices, predictive analytics enables to calculate the likelihood of certain events and the possibility of certain types of customer behavior.

This peculiarity makes this analytical tool highly attractive for businesses, promising them the greater competitive advantage and ability to feel the trends coming. And the key actors in the insurance sector also acknowledge these perks and incorporate predictive analytics into their daily practices.

The role of predictive analytics in the insurance industry

The surfeit of data makes the insurance industry a perfect sphere for predictive analytics implementation. In particular, this tool helps to construct basic patterns and get fundamental insights about the insurance business and manage the complex relations between agents and clients.

Mainly, predictive analytics is used in insurance for forecasting customer behavior. Precisely, the process involves collecting information from various communication channels, interactions with clients, agent feedback, and smart home systems. From all these sources, the analyst gets the raw data to define and improve existing relationships.

In practice, better claims management and clearer underwriting services are the main improvements that predictive analytics brings to the insurance industry. With the application of artificial intelligence, machine learning, and behavioral intelligence, it turns the sphere into a data-driven, predictive, and efficient system.

The common areas for the predictive analytics process

Among all, the appropriate planning of a predictive analytics introduction ensures the insurer will experience all the benefits of this tool. That’s why you should pay precise attention to the areas of implementation. This way, you’ll get the most profit and effectiveness boost.

In essence, the most profitability comes from incorporating predictive analytics into claims management. Also, managing underwriting services will work well.

Start working with these two dimensions while establishing your predictive analytics process. Also, consider the nuances of life insurance and health insurance before building the predictive analytics process.

Predictive Analytics in Life Insurance

In life insurance, key industry actors mostly use predictive analytics to work with Big Data and track any valuable connections. In this context, the insights that are driven from this processing include better addressing of common concerns. They include sophisticated bureaucracy around underwriting and extra time spent on registration.

At the same time, purchases are the blind spot for predictive analytics in life insurance. There exist some difficulties with their modeling because of the lack of needed data in low-frequency periods. That’s why the key emphasis in life insurance analysis is on fixing the underwriting process to date.

Predictive Analytics in Insurance

Predictive Analytics in Health Insurance

Among all, predictive analytics in health insurance supports the key industry actors, like health agencies, hospitals, and medical providers. Its main improvements refer to the areas of business operations, operation accuracies, and treatment adjustments.

Specifically, the application of real-time reporting ensures timely and precise adjustments to the changing environment, including fast adaptation to the changes. At the same time, Deloitte’s report on using predictive analytics in health care warns of possible moral hazards of promising risk control. That’s why the application of this analytical tool calls for maximum caution.

How to get the most from the use of predictive analytics in insurance

Predictive analytics promises two main advantages for the insurance sphere: cost-effectiveness and problem-solving. That’s the reason why this tool is mostly associated with higher profits and better sales figures. However, you won’t be able to enjoy the full package of its advantages without knowing how to apply predictive analytics correctly in the exact insurance areas.

Predictive Analytics Sample Project in Insurance Claim Management

Since predictive analytics brings the most money to insurance businesses by managing claims, we highly recommend incorporating this tool as a project. By this, we mean working with data sets by adopting the “predict and act” approach, incorporating the tools in several stages, and setting effective KPIs.

Among all, by applying predictive analytics, you’ll create the background for better customer experience, greater operational excellence, and improved risk management. For the proper start, IBM Software report recommends adopting a “predict and act” approach to business decision making. It means getting clear answers to 3 questions:

  • How are we doing? (Meaning what the insurance company thinks of itself and what its clients assume)
  • Why? (The answer comes from user-driven top-down analytics and data-driven bottom-up analytics)
  • What should we be doing? (Matching individual level and portfolio level).

With the right and timely answers, you’ll get the sustainable ground for taking effective action from the already connected data. This will let you pass five key decision points:

  1. First notice of loss,
  2. Additional information request,
  3. In-depth investigation,
  4. Payment and closing, and
  5. Subrogation.

Finally, to track your progress, we recommend setting the following KPIs:

  • Reserve adequacy,
  • LAE,
  • Fraud avoided,
  • Investigator efficiency,
  • Amount subrogated,
  • Number of products per policyholder,
  • Customer retention, etc.

Predictive Analytics in Insurance Claims

While claims management is already an integral part of the insurance routine, predictive analytics improves and significantly accelerates its processing. In particular, it enables high client personalization with the clear perks of better time management, cost optimization, and resource control.

These preventive measures ensure customer satisfaction, and the very chances claims will appear in the future are low. Simply put, the longer you use predictive analytics to manage insurance claims, the fewer claims you get in the future.

Predictive Analytics in Insurance Use Cases Challenges

Outlier claims, as a special dimension in claim management, are also handled by predictive analytics effectively. In this case, the tool ensures cost-effectiveness by preventing expensive losses, thanks to the enhanced automation and similarity check. From a long-term perspective, this kind of digitalization enabled effective planning and informed decision-making.

Predictive analytics in the insurance industry: Steps to take

To make sure that predictive analytics in insurance will reveal its best, use this checklist on the necessary measures and tools. There can be many more steps, but these four actions are basic and inevitable.

“What-if” Modeling, or an Alternative Method for Predictive Analytics in Health Insurance 

“What-if” modeling is one of the most effective tools for underwriting preparation. In particular, it helps you check the underwriting workload, produce the registration process, and measure the impact.

Do Market Research

Market research is a critical part of any project strategy. So, don’t hesitate to use enhanced predictive analytical tools to determine your target market. Your data will include behavioral patterns, demographic aspects, and other important characteristics. Everything you need will be on social media, mostly.

Determine the Customer Loyalty Opportunities

Branding activity is an important part of any strategy, and predictive analytics can help here. In this case, your focus here should be on defining and analyzing their needs. An ability to address customer pains in the best manner possible will contribute to your future steady market success.

Determine KPIs

Working with measurable performance indicators makes your business problem achievable. That’s why you should set KPIs as the must-have stage in working with predictive analytics. Use the already collected data to understand your starting points and the milestones you’re about to reach. For example, KPIs for claims management can include reducing claims cycle time, customer satisfaction improvements, fraud control, claim recovery optimization, and savings.

Predictive analytics: Use cases in insurance

There are many good examples of predictive analytics in the insurance industry. In particular, it showed itself effective for data collection, risk management, product optimization, behavioral intelligence, Big Data analysis, and timely resolution of claims. In this section, we’ve collected the top 4 use cases of predictive analytics in insurance.

Pricing and Risk Mitigation

Data insights driven from firsthand information make the decision-making more effective. That’s because the availability of numerous sophisticated data sources enables drawing valid conclusions about customer behavior. Social media posts, smart technologies, and claims are by themselves highly reliable reference points. And the ability to collect and analyze them together in one place provides numerous advantages to insureds.

Customer Care

The great benefit of predictive analytics is in its power to save clients you’re about to lose. The data insights can track visitors who are unhappy or don’t use the insurance service fully and address their concerns long before they become the reason for your breakup.

Fraud Prevention

Predictive analytics has already started handling fraud risk effectively. In this context, social media serves as the perfect source for collecting insights and addressing threats.

Trends Tracking

Predictive analytics can help you keep a competitive advantage by keeping an eye on and notifying about emerging trends. In practical terms, insurers can arrange new products, design customer experiences, and incorporate necessary technological solutions.

Need to empower your insurance company with predictive analytics? Intellectsoft has created impactful solutions for EY, the London Stock Exchange, and EuroAccident. Explore what we offer for the insurance industry.

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