Predictive analytics in insurance: Role and use cases

Recognizing patterns and using them in prognostication has been a human job since we all lived in caves and attempted to predict the weather or animal behavior. Things have changed, and a considerable amount of analytical work is now being carried out by computers and their various algorithms, including pattern prediction. 

Predictive analytics helps many different businesses and industries — healthcare, retail, manufacturing, entertainment, etc. Today, we’ll focus on the use of predictive analytics in the insurance industry – its standard process and the impact on different types of insurance, and will go through the most popular use cases in this industry.  

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What is predictive analytics? 

Predictive analytics is a tool that uses machine learning techniques and statistical algorithms to predict the outcome of different events based on collected data sets and historical records. It’s been around for more than half a century, back when governments had just begun to utilize those gigantic computers for data analysis. However, the processors were too bulky, the software too complicated, and industries lacked technological proficiency, so it wasn’t the time for predictive analytics to shine. Yet.  

Now, with all the advancements in big data, machine learning, data mining, and artificial intelligence, along with less perplexing software and much faster and more accessible computers, predictive analytics have seeped through all kinds of business activities. For example, using this type of analytics for insurance has done wonders, especially with it being such a data-heavy industry. 

Raw data sets usually have little to no meaning to an untrained eye, and even so, manually going through it all can be tedious, time-consuming, and nearly impossible in large sets. Predictive analytics, on the other hand, backed up with various statistical models and techniques, can easily help predict customer behavior, optimize campaigns, manage resources, and expose and prevent criminal wrongdoings.  

Pattern detection presents excellent opportunities for any business that has to make decisions based on previous information, which can be collected from both inside and outside sources. This leads us back to the insurance industry. 

How does predictive analytics impact the insurance industry? 

Any successful operation in the insurance industry consists of two essential elements — reliable information and precise risk assessment. Unfortunately, the abundance of data, albeit important, can cause disruptions in a working process and hinder assessment accuracy instead of assisting it. That explains the immense popularity of analytics tools, not just predictive ones. Due to their unique ability to transform simple information into business intelligence, such tools are valuable to the insurer’s job. 

Despite its unequivocal name, predictive analysis in insurance is so much more than simple data analysis. With its help, insurance agents can transform their data into usable insights not just on clients but on other agents as well, even reshaping their market strategies. By using that insight, insurance companies are able to accurately assess the risk levels of different policies, which will then help establish the price of insurance premiums. 

With predictive analytics, insurance claims can also be made into a faster and much more straightforward process. The more costly a claim will turn out to be, the more losses a company will suffer. By collecting data via multiple sources and designating the estimation process to predictive analytics, insurers can pinpoint trends that were otherwise hidden and anticipate certain events. Besides, with time and rigorous use, analytics can eventually help insurance companies get fewer claims through preventive measures. 

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Predictive analytics process 

To optimize the results of predictive modeling, insurance or any other industry has to go through five essential stages of a typical predictive analytics process. 

1. Identification. During this step, identify the goals and desired outcomes of the project to customize the insurance predictive models. 
2. Information gathering. After collecting necessary information from multiple relevant data sources, submit manually or automatically transfer it from other software and save it into the chosen predictive analytics model. 
3. Cleaning, analysis, and modeling. Next, the data must be cleaned from irrelevant bits, inconsistencies, or duplicates, analyzed, and visualized. Then, through statistical analysis, data sets are made into estimating models. 
4. Testing and deployment. The predictive model is then tested for accuracy and deployed into existing business processes. 
5. Monitoring. This last step involves routine assessments to ensure the model’s efficiency and accuracy in various scenarios.  

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Predictive analytics in health insurance

One of the staples of actuarial activity and yet, not particularly favorable among insurers, health insurance is that type of business-client relationship where you don’t usually share positive news. Whether it’s an illness, an accident, or simply precautionary measures, an interaction is far from pleasant.  

In the case of health insurance, predictive analytics is focused more on preventing that final interaction and improving or optimizing the customer experience along the way. The best use of predictive models results from estimating the level of risk that comes with providing health insurance plans to certain individuals. These plans need to be accurately priced, depending on the person’s eligibility and previous behavior patterns.  

Previously, when insurers operated without advanced analytics, earlier questionable behavior of a potential client meant no deal. Now it means the right coverage plan for every person. Coverage adjustments can also stem from the predictions of how often a person will need to visit a doctor, and the history of previous injuries, which in turn will help calculate exact costs. 

Fraud in health insurance is a serious issue that predictive models can help you prevent. Only in the USA, the damage from healthcare fraud amounts to nearly $230 billion a year. By monitoring behavior, it’s quite possible to prevent scams altogether. 

Predictive analytics in the life insurance industry 

The decisions made with the help of predictive analytics provide a more accurate analysis of many standard variables of life insurance policies, such as drug combinations, dosage, frequency of use, a person’s gender, age, the severity of conditions, other health decisions, behavior, and common patterns.  

One of the lesser-known life insurance predictive analytics examples is assessing more than just the individual’s behavior or medications. Some models, combined with genetic profiling, can help adjust policies by differentiating risks caused by unhealthy lifestyles or haphazard work environments from genetic disorders that are beyond anyone’s control. 

Aside from the assessment process, predictive models can help with customer acquisition in life insurance — optimize marketing campaigns, and reduce their costs. For example, predictive models for prospect scoring can scan psychographic data, texts, web log data, surveys, and purchasing information to determine the potential to convert.  

Another benefit comes from potentially reducing policy lapses during the first couple of policy years. That way, insurers can ensure the recovery of acquisition costs by preventing the premature existence of the policyholder. 

Use of predictive analytics in insurance industry 

Besides using predictive analytics in insurance claims, a few other cases have gained popularity in this industry, and deservedly so. Let’s focus on some of the most beneficial ones. 

1. Anti-fraud activities  

According to the American Insurers Association’s fraud statistics, 10% of all claims prove to be fraudulent, while this article even estimates using newer data that every 5 minutes, a fraudulent claim gets discovered in the world. With the help of predictive analytics, there’s hope these numbers will go down. 

For effective fraud prevention, predictive analytics have already started analyzing various social media outlets to monitor online activity in case of any red flags. By using multiple external and independent sources, this software can study the individual’s behavior across every outlet, note the frequency of previous claims, as well as credit score and overall reputation.  

2. Customer support  

Next in our insurance predictive analytics examples list comes customer support, which plays a colossal role in customer retention. Using insights from predictive analytics tools, you can minimize quitting by picking up the first signs of client dissatisfaction — initial complaints and non-vocalized concerns, which you can track by scanning the usual habitual patterns. 

Besides retention, customer support, enhanced with predictive analytics insights, can also increase loyalty by better anticipating clients’ needs and personalizing the whole experience. Alternatively, you can test out potential services or changes to the existing coverages according to someone’s buying habits without actually testing them on customers. 

3. Risk management  

Risk management is one of the vital elements of the insurance process. Multiple related and unrelated factors come into play while assessing whether a person or a situation is high or low risk. The level of combined risk is what determines how much you’ll decidedly pay for a premium. 

Predictive analytics improves the overall accuracy of such assessments. Underwriters go through tons of structured and unstructured data to develop insight and base their decision on it. Analytics automates this process and helps receive more consistent results. 

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4. Behavior prediction  

One of the most prominent uses of predictive analytics for insurance goes along with behavior prediction. Similar to its use in customer support, analyzing and predicting human behavior and hidden intentions can save insurance companies a lot of headaches by avoiding unexpected scenarios. 

With the help of predictive analytics, behavior gets a little less fickle and chancy since you can see what causes a response based on previous instances of purchases, credits, or fraud. Behavior prediction also benefits from an uplift modeling technique, where you can identify individuals who are more persuasive than others. 

5. Reporting and decision-making 

When a business heavily relies on data reporting and decision-making as insurance does, it’s better to have and feel sizeable support from analytics and a statistical advantage. Here are the signs that you can improve decision-making and subsequent reporting if you use predictive analytics: 

• When the data quality is more important than the sheer quantity of it 
• Performance depends on prediction accuracy
• Data comes from different sources
• Utmost dependence on personalized information

The one major difference predictive analytics can make for your insurance business is to make it more proactive. Rather than storing your data with no actual use and waiting for losses to happen and then compensating for them, you can utilize especially valuable in the insurance industry data analytics to gain a competitive advantage. Click here if you’d like to have a free consultation with our team to learn more about this type of software. 

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