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11 Keys to Predictive Analytics in 2021

According to Willis Towers Watson, more than two-thirds of insurers credit predictive analytics with reducing issues and underwriting expenses, and 60% say the resulting data has helped increase sales and profitability.

That figure is expected to grow significantly over the next year, as the inherent value of predictive analytics in insurance is showing itself in myriad applications.

Predictive analytics tools can now collect data from a variety of sources – both internal and external – to better understand and predict the behavior of insureds. Property and casualty insurance companies are collecting data from telematics, agent interactions, customer interactions, smart homes and even social media to better understand and manage their relationships, claims and underwriting.

Another closely related tool is predictive modeling in insurance, such as using “what-if” modeling, which allows insurers to prepare for the underwriting workload, produce data for filings and evaluate the impact of a change on an insurer’s book of business. The COVID-19 crisis has shown insurers that the ability to predict change is invaluable, and “what-if” modeling is a great tool for carriers that know they need to make changes but want to ensure they are doing it accurately. The right predictive modeling in insurance software can help define and deliver rate changes and new products more efficiently.

Using the plethora of data now available, here are 11 ways predictive analytics in P&C insurance will change the game in 2021.

Pricing and Risk Selection

This isn’t exactly a new use for predictive analytics in insurance, but pricing and risk selection will see improvement thanks to better data insights in 2021. Given the increased variety and sophistication of data sources, information collected by insurers will be more actionable.

Why do these data sets help predictive analytics improve pricing and risk selection? Because they are largely composed of first-hand information. Data and feedback collected from social media, smart devices and interactions between claims specialists and customers is straight from the source. Data that isn’t harvested through outside channels (such as the typical demographic material used in the past, like criminal records, credit history, etc.) is more direct and can provide valuable insights for P&C insurers.

But just how much data are insurers collecting from IoT-enabled devices? Some reports estimate it’s approximately 10 megabytes of data per household, per day, and that figure is expected to increase.

Identifying Customers at Risk of Cancellation

Predictive analytics in P&C insurance is going to help carriers identify many customers who require unique attention – for example, those likely to cancel or lower coverage. More advanced data insights will help insurers identify customers who may be unhappy with their coverage or their carrier.

Having this knowledge in hand will put carriers ahead of the game and allow them to reach out and provide personalized attention to alleviate potential issues. Without predictive analytics, insurers could miss credible warning signs and lose valuable time that could be used to remedy any issues.

Identifying Risk of Fraud

P&C insurance companies are always battling various instances of fraud and oftentimes aren’t as successful as they would like. The Coalition of Insurance Fraud estimates that $80 billion is lost annually from fraudulent claims in the U.S. alone. Additionally, fraud makes up 5% to 10% of claims costs for insurers in the U.S. and Canada.

Using predictive analytics, carriers can identify and prevent fraud or retroactively pursue corrective measures. Many insurers turn to social media for signs of fraudulent behavior, using data gathered after a claim is settled to monitor insureds’ online activity for red flags.

Insurers are also relying on insurance predictive modeling for fraud detection. “Where humans fail, big data and predictive modeling can identify mismatches between the insured party, third parties involved in the claim (e.g. repair shops) and even the insured party’s social media accounts and online activity,” according to SmartDataCollective.

See also: What Predictive Analytics Is Reshaping

Triaging Claims

Customers are always looking for fast, personalized service. In the P&C insurance industry, that can sometimes present a challenge. But with good predictive analytics systems, carriers will be able to prioritize certain claims to save time, money and resources – not to mention retain business and increase customer satisfaction.

Predictive analytics tools can anticipate an insured’s needs, alleviating their concerns and improving their relationship with their carrier. It can also contribute to tighter management of budgets by employing forecasted data regarding claims, giving insurers a strategic advantage.

Focusing on Customer Loyalty

Brand loyalty is important, no matter the product, and now insurers can use predictive analytics to focus on the history and behavior of loyal customers and anticipate what their needs may be. How important is brand loyalty? About half of customers have left a company for a competitor that better suited their needs. Also, this data can help insurers modify their current process or products.

Identifying Outlier Claims

Predictive analytics in insurance can help identify claims that unexpectedly become high-cost losses — often referred to as outlier claims. With proper analytics tools, P&C insurers can review previous claims for similarities – and send alerts to claims specialists – automatically. Advanced notice of potential losses or related complications can help insurers cut down on these outlier claims.

Predictive analytics for outlier claims don’t have to come into play only after a claim has been filed, either; insurance companies can also use lessons learned from outlier claim data preemptively to create plans for handling similar claims in the future.

Transforming the Claims Process

With predictive analytics, insurers can use data to determine events, information or other factors that could affect the outcome of claims. This can streamline the process – which traditionally took weeks and even months – and help the claims department mitigate risks. This also allows insurers to analyze their claims processes based on historical data and make informed decisions to enhance efficiency.

Advancements in artificial intelligence and other analytical tools have also become increasingly important in the claims process and are transforming how carriers do business.

Data Management and Modeling

Data is one of the most valuable assets an insurer can have, and predictive analytics have been helping businesses make the most of that data. From forecasting customer behavior to supporting underwriting processes, predictive analytics and data have been working together to provide valuable insights to insurers for years now.

However, making the most of your data is only possible with excellent data management and modeling capabilities. If data is scattered across disparate systems and there isn’t a strategic plan in place, all of that data is wasted. With data management solutions, predictive analytics tools can build a robust customer profile, provide cross-sell and upsell opportunities or even forecast potential customer profitability. And with insurance data models, insurers can deliver on-demand services to their customers via the cloud, using the data-driven insights gathered from their data management platforms.

Identifying Potential Markets

Predictive analytics in insurance can help insurers identify and target potential markets. Data can reveal behavior patterns and common demographics and characteristics, so insurers know where to target their marketing efforts.

Because there are 3.2 billion people on social media around the world, these platforms have become increasingly important when it comes to identifying potential markets. The platforms also influenced customer service: about 60% of Americans say that social media has made it easier to obtain answers and resolve problems.

Gain a 360-Degree View of Customers

TechTarget defines the 360-degree view of a customer as “the idea that companies can get a complete view of customers by aggregating data from the various touch points that a customer may use to contact a company to purchase products and receive service and support.”

Using predictive analytics, insurers can quickly and accurately consolidate data and generate insights that paint a more complete picture of a customer. What are their buying habits? What is their risk profile? How apt are they to buy new or expanded coverage? Before predictive analytics, insurers could estimate or take guesses at these questions, but now they are able to accurately and effectively service customers, which ultimately results in happier customers and increased revenue.

See also: How Analytics Can Tame ‘Social Inflation’

Providing a Personalized Experience

Many consumers value a customized experience – even when it comes to shopping for insurance. Predictive analytics in insurance provides the capability to comb through IoT-enabled data to understand the needs, desires and advice of their customers.

More and more insurers will use predictive analytics to help forecast events and gain actionable insights into all aspects of their businesses. Doing so provides a competitive advantage that saves time, money and resources, while helping carriers more effectively plan for a future defined by change. After all, data is only a strategic asset when you can actually put it to work.

What Predictive Analytics Is Reshaping

Insurance is a business sector where predictive analytics software has some of the most straightforward applications, also with a high return on investment (ROI). Predictive analytics is already offering companies significant savings, and it is expected to grow exponentially in the next few years. Most likely, it will become the standard practice for insurance and risk management.

The advantage is that the data lake used for predictive analytics can collect both internal and external data and correlate it to identify patterns and create almost real-time reports. In turn, this would prevent fraud and help to analyze behaviors.

The results can be used in various areas of the business, which include risk assessment, pricing policies, claim processing, fraud management and trend analysis. Here are a few of the ways predictive analytics is reshaping the insurance sector:


This is one of the first applications of predictive analytics in the insurance sector because it offers a high ROI. As sources become more diverse and precise, results will be more actionable. Although there are relevant security and privacy issues involved, insurance companies are collecting and analyzing data from sources that, for example, 10 years ago were not available or considered relevant, like social media.

The good news is that now data is no longer an average of a cluster, but, after the general profile is created, the machine can measure how each person scores against the grid.

Market Trends and Risk Assessment

Identifying market trends is all about detecting the right patterns in data and anticipating their further development. Fortunately, AI is perfect for doing just that, regardless of the volume or complexity of the input data.

Recently, both the U.S. government and the E.U. ruling organs have adopted an “open data” policy, making available lots of census data related to population statistics, education, safety and more. These new sets offer insurance companies new opportunities regarding macro risk assessment.

See also: 3 Ways to Optimize Predictive Analytics  

Correlating these sets of data within the right algorithm can help insurance companies to create clusters of customers grouped according to their profitability. For example, such analysis can provide the answers to questions like the probability of a person being involved in a car accident in a certain town, or the likelihood of default for a mortgage for a specific educational profile.

The next step is to extrapolate the results and make predictions for the following periods to stay ahead of the market.

Fraud Detection and Prevention

Insurance is a very vulnerable sector for fraud. People are tempted to pay for an insurance policy and “make it look like an accident” to collect the value of the insurance. Although over the years insurance inspectors have become well aware of classic schemes, new tools are needed because the insured risks become more diverse and linked to digital activity.

The Coalition of Insurance Fraud estimates that over $80 billion is lost due to fraud. The same studies show that one in 10 claims is fraudulent. Therefore, insurers are ready to go to any lengths necessary to prevent such actions.

The advantage of predictive analytics is that it can signal potential fraud before it happens. The machine would identify specific patterns associated with fraud, usually by means of dots that don’t connect.

Tailor-Made Services

Most companies, from utilities to retail and especially e-commerce, strive to offer customers a very personalized experience. The insurance sector needs to be at the forefront of this practice, too, as products have few real differentiators apart from the price.

In this business, predictive analytics can look at customers’ profiles and predict needs, create bundles of services and help these customers meet their personal goals. Depending on a customer’s profile, such purposes can include increased safety, budget management, saved time or significant risk hedging.

These systems also offer the opportunity to prioritize claims and serve customers not only in their arriving order but also by evaluating their lifetime value, to avoid losing important ones who need their cases sorted faster.

Customer Retention

Learning from the world of retail and even HR, the insurance business can benefit significantly from identifying those customers who are about to cancel their policy. Usually, by giving these some extra attention, they can be kept onboard for another year or more.

In this case, data insights and customer behavior analysis can help insurance companies identify those who are already looking for solutions from competitors.

Focus on the Extraordinary

Not all odd claims are frauds, but unexpected and expensive claims can hurt an insurance company’s profit margins. In this case, the role of predictive analysis is to identify potential risks and warn the customer to take all necessary preventive measures. Although such outliers are harder to detect due to the lack of previous relevant data for training, the advantage of using machine learning is that it can put together several distinct pieces of information to identify potential risk.

See also: 3 Key Steps for Predictive Analytics  

Privacy Concerns

As in all matters related to the use of personal data, some people could have three categories of concerns, as stated by the report of the Geneva Association. To wrap up this discussion of data-centric insurance, let’s look at them:

  • Privacy and data protection concerns. These are mostly related to the fear of discrimination based on profiling. The other problem in this category is intrusiveness in the right of self-determination, especially when customers can’t afford the prime for their risk class, thus restricting their lifestyle options.
  • The individualism of insurance problems. The problem of exclusion should be at the forefront of insurance companies’ internal regulations. Excluding certain high-risk categories can lead to social pressure and the need to find alternative solutions such as state funding.
  • Implications of big data and AI for competition. The fourth technological revolution is already causing disruption and changing markets. By implementing these tools, we can expect that some jobs will disappear or reorganize. This will also happen to companies that will not adopt the new standards.

It’s Time to End Appeals Based on Fear

Consumer attitudes toward the insurance industry are changing faster than ever. Millennials make up the most populous generation today, and with many of them entering their mid- and late thirties, they are shopping for insurance in higher numbers. This tech-savvy generation expects personalized services and demands greater control over their experiences and decisions. Millennial consumers are calling the shots in almost every B2C industry – and insurance is no exception.

The insurance industry traditionally relied on the fear of the unknown as its most powerful sales enabler, but with millennials making decisions based on brand experience, insurers need to turn to emerging technologies to transform and customize the way they reach customers. The status quo is simply unsustainable if they want growth. Forward-looking insurers know that the key to attracting and retaining clients is to leverage predictive technology and provide them with the seamless, smart, digital-first experience they need.

But for this future to become a reality, companies need to implement and use predictive analytics in a way that truly enhances the customer experience. Here are the steps every insurer needs to know before embarking on that journey:

Collect the Right – Not the Most – Data

Knowing the ins and outs of customer needs and behaviors is essential in operating an insurance business, but it is not enough to know the general needs of a customer base. In fact, the majority of consumers are willing to share personal information in exchange for added benefits like enhanced risk protection, risk avoidance or bundled pricing. To deliver personalized service, insurers must collect data at the individual level – and quantity does not always mean quality. The accuracy of predictive analytics relies on the certainty and relevancy of the data those systems are fed. Before doing anything else, insurers must determine exactly what information drives business decisions and collect that data on both individual and grand scale as efficiently as possible.

See also: 3 Ways to Optimize Predictive Analytics  

This is where the Internet of Things (IoT) steps in. As one of the most ground-breaking technologies on the market today, IoT has only just begun to realize its potential in the insurance industry. IoT sensors attached to infrastructure, cars, homes and other insurable items, can feed real-time data back to providers with unprecedented accuracy. Not only does this live feed of data prevent emergencies by identifying potential problems before they arise, the highly precise information acts as a foundation for analytics at a customer-specific level in the next phase of the process.

Get Personal With Predictions

Once insurers are collecting relevant, accurate and individualized data, the next step on the road to customer satisfaction is applying machine learning and AI to that information. The outcomes of this analysis not only determine truths about the current status of an asset or situation but reveal patterns that enable insurance companies to predict what is in store down the road. For an insurer, this predictive knowledge means more accurately being able to evaluate, price and plan for risk – whether evaluating individual portfolios or aggregating data to foresee larger trends in the marketplace.

But as predictive technology becomes more mainstream, the true value of digital foresight will be its ability to offer the millennial customers the deep personalization and hyper-relevance they crave and expect from all their services. By transforming the industry into a predictive and even preventative experience, insurance companies are changing the status quo of fear-based customer relationships and instead leverage technology to make insurance feel tailored and assuring.

Engage With Emerging Technology

The insurance industry is not and never will be based on static, one-time decisions. As risk is calculated on various constantly changing variables, it is essential to continue evolving customer predictions, recommendations and prices based on incoming information. Analyzing both existing and new data from IoT sensors allows companies to pivot strategies in the face of new predictions, enhance underwriting, reduce claim ratio and remain agile to meet the needs of their customers today and tomorrow.

See also: What Comes After Predictive Analytics  

Just as predictions do not stand still, neither should an insurance company’s methods for determining them. In an era of hyper customer relevance, with disruptive players like Uber, Venmo and Mint, millennials have come to expect services that are not only predictive but get deeply personalized in accuracy and usability overtime. The insurance industry has traditionally lagged behind other B2C industries in terms of adoption, however, due to its changing customer base it will have no other choice than to evolve rapidly over the next few years. Placing emerging technologies like AI, machine learning, automation and IoT at the core of business operations now will be key in setting insurance up for continued progression in the future.

Appealing to the new generation of insurance customer is all about offering tailored experiences that cater to their needs and expectations. The insurance industry is in for an acceleration of change to accommodate their new millennial consumer – a change fueled by technology that creates bonds of loyalty and trust via personalization, not fear.

3 Ways to Optimize Predictive Analytics

A few years ago, simply applying predictive analytics to insurers’ underwriting practice was enough to gain a competitive edge against the large portion of the market that was still operating with traditional methods. That ship has sailed with increased adoption of analytics, raising the stakes for companies that once enjoyed a first mover advantage. Currently, 60% of insurers have welcomed predictive analytics into decision-making and underwriting processes, and research continues to show correlation between predictive analytics integration in the property & casualty industry and improvement to top and bottom lines. Companies that view analytics as a necessary commodity for modern underwriting instead of the centerpiece to their decision making will find themselves falling short of their competition. The biggest differences between the winners and losers in analytics today is equal parts ideological and technical.

In its recently published ROI study, Valen Analytics observed 20 insurance companies, representing $1.8 billion in premium, and compared their loss ratios and premium growth against the industry. The study showed that data-driven insurers consistently outperformed the market on both metrics.

  • Between 2012 and 2017, the industry saw its loss ratios improve by 18 points, whereas these 20 carriers averaged improvements that were nearly twice that (loss ratios improved by 35 points).
  • Between 2012 and 2017, industry-wide premium grew 18% on average, while the carriers studied grew by 53%.

For the first time since its inception, the ROI study isolated the impact of applied analytics on insurers with concerning loss ratios: those whose loss ratio were greater than 60%. This group of insurers saw loss ratios improve to market average within 12 months, and then outperform the market with each subsequent year. These results underscore the value of predictive analytics in insurance.

See also: 3-Step Approach to Big Data Analytics  

Below are three best practices that the insurers studied have implemented to draw the most value from their predictive analytics programs:

Empower underwriters

The considerably positive findings of Valen’s study do not imply that predictive analytics should replace traditional underwriters. Instead, research suggests that predictive analytics tools should aid traditional insurance writers. This year’s study found that underwriter performance improves 3x when they combine predictive analytics with expertise. A well-implemented analytics solution helps underwriters leverage powerful data that they wouldn’t be able to otherwise, and underwriters provide the expertise to make the final decision. In other words, an insurance underwriter’s wealth of knowledge and contextual expertise is a largely irreplaceable asset. Underwriters know the critical variances between the price suggested by the analytics model and the historical habits of a policyholder and can incorporate this information into their decisions. Thus, predictive analytics usage augments an underwriter’s decision-making process rather than supplements it.

Streamline the workflow

Predictive analytics enable insurers to accurately align price to risk exposure, helping underwriters price policies within the context of an insurer’s risk appetite, and oftentimes allowing insurers to implement straight-through-processing (STP) for specific types of risk. In doing so, insurers can eliminate the need for underwriters to be heavily involved in certain decisions and allow them to focus on the decisions that will have the greatest impact to a book of business. This, again, leverages the expertise of an underwriter.

Incorporate the right data

Insurers that have incorporated a consortium of anonymized data into their model-building initiatives tend to be better-positioned for growth. This additional information can be crucial to initiatives like expansion across states or business classes, often by identifying risks that might fall in a blind spot of institutional knowledge. In other cases, the incorporation of consortium data will eliminate sample bias in an existing book of business. For instance, an insurer that’s relied heavily on its expertise in knowing how to underwrite low-risk construction accounts in one state to build a data set that determines good risks in a new state will risk overfitting the model, essentially giving it too high a standard. This will leave an insurer vulnerable to underpricing risky accounts without third party data to balance the scales. Consortium data increases the predictive power of models and helped the group in our ROI study of analytically inclined insurers grow premium last year, even as the market declined.

See also: Global Trend Map No. 5: Analytics and AI  

For the third consecutive year, Valen’s ROI study has identified just how much value applied analytics can add to insurers. The carriers that have leveraged analytics and consortium data and empowered their underwriters have realized significant advantages over competitors to improve both profitability and growth.

Insurance and the Internet of Things

For many, the concept of a “smart home” is a futuristic, and perhaps even frivolous, offering where lights shut off automatically once we fall asleep, thermostats are controlled from your phone and security cameras can show you what’s going on in your home from thousands of miles away. However, as I have written in many previous posts, we are only at the start of the Internet of Things (IoT). Significantly more sophisticated devices are already entering the market and soon consumers will see the benefits of both enhanced personal safety and home protection. Forward-thinking insurance companies are not only recognizing the potential for reduction in non-catastrophic loses, they are embracing the potential by filing smart home discounts to create incentives for consumers who use these technologies.

Let’s look at a few of these enabling technologies and their potential for loss reduction/avoidance around the core perils of water, fire and theft:

1) Advanced home security products — The professionally monitored home security market has limited penetration in the U.S. — a significant number of home owners don’t feel the need to have their homes monitored for theft. However, many IoT devices enable basic self-monitoring features as a secondary benefit. From video cameras with 24×7 recording, to controllable door locks, to lights that are triggered on with motion, home owners are now getting home security features included with IoT products that might otherwise be purchased primarily for convenience.

2) Leak detection — Traditionally, these products focus on single points of failure, providing coverage in specific locations, such as below a dishwasher or a hot water tank. While providing a lot of utility relative to their cost, it’s been hard to programmatically prove loss reduction with these devices as the location of the sensor has so much to do with catching the leak. That said, more ambitious forms of leak detectors are entering the market, enabling whole-home monitoring, from flow sensors installed on mains, to lightweight stripping that can be installed in floor boards. Additionally, a series of whole-home shut-off valves are also being introduced into the market. Most of these valves require professional installation; however, they are capable of automatically closing the water main with the slightest detection of a leak or abnormal usage patterns. Water losses may be greatly reduced if a home could automatically respond to a burst pipe or an overflowed toilet.

3) Connected smoke alarms and “listeners” — Fire alarms have saved many lives, but the original design was intended to notify occupants of a fire so they could quickly exit. Unfortunately, if no one is home to hear a smoke alarm, there isn’t much that can be done by way of stopping a fire before a total loss. But the new generation of connected smoke alarms and “listeners” (an add-on that hears an alarm and sends a signal) can message not only the home owner but also a third party who can dispatch emergency crews on a homeowner’s behalf. It’s not hard to imagine how dramatic loss reductions will be when all homes have connected fire safety devices.

An exciting aspect of all of these enhancements is that they are incremental improvements on already approved safety devices, enabling a fast track of the actuarial analysis/regulatory acceptance of additional discounts. But these improvements are just the start…

See also: Global Trend Map No. 7: Internet of Things  

Connected devices are particularly special because the “intelligence” doesn’t necessarily need to reside on the device itself, but could also live in the cloud, where processing is getting more powerful and less expensive by the day. As such, there is a tremendous amount of innovation in the data analytics space — and here are a few technologies that will almost certainly result in greater loss reduction:

1) Real-time analytics — the more information that can be analyzed in real time, be it from multiple sensors or devices or historical data, the higher the accuracy in early detection of a potential loss situation. For instance, a sharp rise on a temperature sensor might indicate a fire, but it also could be caused from sunlight striking the device. Long-term tracking of that temperature data might quickly indicate what is normal, what is not. Or perhaps a flow sensor might detect a flow of water similar to shower running, but when paired with alarm system that shows the home is unoccupied and the alarm has been in “away” mode for several days could be a clear indication of a burst pipe.

2) Automated response logic — connected devices lend themselves well for automated responses. Homeowners will be able to create steps that are enacted when emergencies are detected. For instance, when a fire alarm rings, the sequence might be something like: a) snap a picture from each camera and take a temperature read from each sensor in the house, b) email/text all of the family that lives there with the data to confirm or override an emergency call, c) if no response within 60 seconds, forward the notifications to a third party for emergency dispatch. Automation combined with human intervention allows for a more accurate and effective response.

3) Predictive analytics — ultimately the best way to lower losses is to prevent problems before they start. This is where heavy processing power is required — as well as buy-in from consumers on the use of their data. Connected homes provide streams of output data and, with it, anticipated performance. Variances in this data might indicate early stages of problems. For instance, a packaged HVAC system might be showing degradation of airflow in the summer, which could mean trouble for gas heating as temperatures drop. It might be in the best interest of the insurance company to ensure performance is restored as the winter comes, prior to the risk of freezing pipes. Additionally, as we are seeing in telematics and auto insurance, you can bet that consumer behaviors will also have the potential to be analyzed, no doubt showing correlation between “safe” homeowners and reduced loss.

While more forward-facing than the device enhancements listed in the first section of this article, it’s these enhanced intelligence features that will truly revolutionize loss models. The more advanced the technology becomes, the less dependent the loss prevention becomes on human behaviors.

See also: Insurance and the Internet of Things  

Imagine a world where the main perils for homeowners insurance carriers such as water, fire and theft are dramatically reduced through the IoT and smart homes. Yes, consumer mistakes/negligence, even moral hazard, will always be an issue, but at some point it’s very possible the home will become smart enough to compensate even for these factors in a substantial way. We are already seeing rapid advancements in these areas in both telematics for auto insurance and wearables for life and health insurance. Similarly with smart homes, these IoT technologies have significant potential to lower losses from non-cat perils.