Predictive analytics is transforming the insurance industry by enhancing pricing and risk selection, identifying customers at risk of cancellation, detecting fraud, improving claims processing, and boosting customer loyalty. The potential use cases of predictive analytics in insurance are varied, offering a wide range of benefits to policyholders and insurance carriers.
In this guide, we’ll take a closer look at the rise of predictive analytics in the insurance industry. We’ll explore the different ways carriers can leverage this technology to improve operations, including best practices for overcoming potential roadblocks during implementation.
What is Predictive Analytics?
Predictive analytics is a type of business intelligence that leverages advanced algorithms and comprehensive datasets to predict future trends, events, and behaviors. This includes both historical and real-time data in structured and unstructured formats.
Predictive analytics determines the probability of certain outcomes by analyzing and identifying patterns in current data and using machine learning (ML) and statistical modeling to predict probable scenarios.
The Growing Role of Predictive Analytics in Insurance
While not exclusive to the insurance space, predictive analytics has a growing list of potential applications in the industry. In fact, 67% of insurers say they plan to increase spending on data analytics technology.
With the potential to augment or automate a range of existing insurance workflows, predictive analytics models are proving highly useful to insurance carriers. These companies have always relied on historical data to inform strategies and risk management. Now, predictive analytics enable insurers to consider much larger datasets and make predictions with a higher degree of accuracy and reliability.
The Benefits of Predictive Analytics for Insurers
The adoption of predictive analytics in insurance is still in the early stages. However, insurance carriers are already seeing significant benefits from implementing these models, such as:
Enhanced Operational Efficiency
A large advantage of predictive analytics is that it enables insurance teams to work more efficiently. These models can process large volumes of data and make future predictions much quicker than is possible through human efforts alone.
For example, predictive analytics can help streamline aspects of key insurance workflows like underwriting, claims management, fraud detection, and policyholder engagement. As a result, staff members have more time to focus on their core activities without compromising the quality of these workflows.
Improved Policyholder Experience
Predictive analytics also enables insurance carriers to deploy personalization at scale, helping to improve policyholder engagement, satisfaction, and retention. This technology can use current data from Internet-of-Things (IoT) devices, social media, and policyholder interactions to create comprehensive customer profiles and anticipate future needs.
These insights enable personalized experiences and better targeting, helping to meet policyholder wants and needs and differentiate carriers from competitors. This might mean engaging in cross-selling or upselling when relevant to current policyholder needs, helping to stay relevant and provide a convenient customer experience.
Informed Decision-Making
Previously, making predictions about future trends and policyholder behaviors relied partly on limited historical data and the team’s intuition. Now, insurance carriers can rely on advanced algorithms and Big Data for more accurate and reliable forecasts, driving informed decision-making.
Depending on the specific model, it can offer a wide range of insights about future conditions to inform current strategies. For instance, considering the possibility of a policyholder filing a claim, the likelihood of churn, the chances of an economic downturn, and more.
Potential Cost Savings
Lastly, insurance carriers may be able to achieve significant cost savings with the help of predictive analytics. As we’ll explore in further detail below, this technology can improve the accuracy of claims fraud detection, preventing carriers from making fraudulent payouts.
Insurance fraud costs American consumers $308.6 billion each year. Using predictive analytics to prevent insurance fraud can therefore produce widespread savings.
Additionally, since predictive analytics can help insurance teams work more accurately and efficiently across key workflows, it can reduce the cost of human error throughout operations, not just in claims processing.
5 Key Applications of Predictive Analytics in Insurance
Insurance carriers can leverage predictive analytics in a number of ways depending on their specific needs, objectives, and resources. Here are a few common examples of how this technology can be used to enhance insurance operations:
1. Fraud Detection
The insurance industry loses billions each year to fraudulent claims. As fraudsters’ tactics become more sophisticated with the help of emerging technologies, it makes sense that insurance carriers would leverage advanced solutions like predictive analytics to combat these rising threats.
Insurance companies can train models to detect normal claims patterns and flag outliers with anomalous behaviors. Over time, these models will become more adept at understanding what’s expected behavior, flagging potential fraud that may have otherwise gone undetected.
2. Underwriting
Accurate risk assessments are the foundation of underwriting operations. So having tools that can assess large volumes of historical data to reliably predict future risks is highly advantageous. Compared to traditional underwriting practices, predictive analytics models allow insurers to consider hundreds of data points from a wide range of sources to create more accurate risk profiles.
These advanced analytics offer underwriters greater visibility into the potential risks of covering each individual policyholder. In turn, teams can make more informed coverage and pricing decisions to ensure the premiums adequately cover each policyholder’s expected loss generation.
3. Claims Management
While predictive analytics is largely associated with fraud detection in the claims management process, it can also be used to streamline other aspects of the workflow. When trained properly, these systems can accurately assess the details of a claim and segment them into standard or complex categories, ensuring that they’re assigned to the appropriate claims specialist.
The result is that teams can utilize their resources more efficiently, with the most experienced adjusters focusing on the most complex claims and more standard cases handled by automated systems or delegated to junior staff. Plus, it enables teams to reach a settlement quicker, supporting better policyholder satisfaction.
4. Loss Prevention
One of the more unique applications of predictive analytics in the insurance industry is integrating with IoT and telematics devices as a way to prevent losses and damages from occurring, thereby preventing related claims payouts.
For instance, IoT devices combined with predictive analytics can analyze real-time data on critical home systems, working to identify normal patterns and predict possible issues. This helps policyholders and insurers be more proactive by alerting them of equipment failures, water leaks, and fire hazards before they cause property damage.
5. Anticipating Policyholder Churn
Predictive analytics can also help insurance carriers identify which customers are more likely to cancel or not renew their policies. These insights can make it easier for carriers to understand which policyholders require intervention to mitigate churn risk and regain loyalty.
Insurance companies can use these models to analyze policyholder behaviors and spot activities that may indicate they plan to take their business elsewhere. This might include reduced engagement, delayed payments, or leaving negative reviews online.
Challenges of Implementing Predictive Analytics in Insurance
Before insurance carriers can enjoy the significant benefits of predictive analytics, they’ll need to strategize how to overcome some of the common challenges to successful implementation.
Lengthy Implementation
Implementing predictive analytics in operations won’t happen overnight. Teams should be prepared to commit time and resources to the project to ensure successful implementation. For busy insurance teams that already have a full schedule, this might be a daunting task, especially if it takes away from their ability to focus on core competencies.
In particular, insurance carriers that still rely on legacy systems may face additional difficulties during data migration. They’ll need to cleanse and format large volumes of historical data so it’s compatible with the new technology and generates reliable outputs.
Team Resistance
Predictive analytics is still a relatively new technology and has a short history in the insurance space. It can be difficult to make a business case for leadership to embrace this technology, especially given the resources required for implementation.
At the same time, those involved in the workflows where this technology might be implemented may be resistant to the change. Staff members who are used to doing things a certain way may see the new technology as a threat to their position.
Plus, teams may not feel comfortable trusting predictive analytics models to inform important decisions regarding risk management, policy pricing, etc., without having seen the technology in action.
Data Privacy Concerns
For best accuracy, predictive analytics models must have access to a large volume of data, some of which might include sensitive information. The growing use of this technology has been met with ethical and data privacy concerns among consumers, regulators, and staff regarding how insurance carriers collect, manage, and utilize consumer data.
Certain data privacy laws in the United States and abroad already address how organizations collect, store, and share personal information. Thus, insurance carriers must ensure they’re meeting data privacy compliance requirements throughout its implementation and use.
Best Practices for Leveraging Predictive Analytics in Insurance
With the following best practices, insurance carriers can get the most out of predictive analytics tools:
Have a Clear Goal
Before employing predictive analytics, insurance carriers should clearly define what they are hoping to achieve with this technology. Predictive models are most effective when designed and trained to handle a specific question or challenge—like detecting claims fraud risk. Trying to use a single model to support multiple objectives can dilute its effectiveness.
Ensure Data Quality
The quality of outputs from predictive analytics models is only as good as the quality of data put into them. For accurate results, insurance carriers must ensure the datasets fed to these models are properly formatted, accurate, and comprehensive.
Otherwise, the outputs could be unfair, inaccurate, and misleading, leading to serious consequences for the insurance carrier—like policyholder frustration, non-compliance, and misguided risk management decisions.
Continuously Monitor Outputs
Data analytics is superior to manual processing because it helps eliminate the risk of human error. However, these systems should not be seen as infallible. There is still a chance of inaccuracies and bias in their outputs.
Teams should continuously monitor how models perform to ensure they’re generating expected results, making tweaks as needed to improve their accuracy and reliability over time.
Leverage Advanced Insurance Analytics with Insuresoft
With Insuresoft’s insurance data analytics software, insurance carriers can access real-time data and operational analytics from one convenient dashboard.
Whether you want to use analytics to boost performance or enhance efficiency, Insuresoft can help. We enable you to derive data-driven insights from internal and third-party sources for better decision-making, helping you evolve for the digital age.
Contact us to learn more about our data analytics hub.
