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The insurance business is a highly competitive and risky industry that relies on hard data. The best way to mitigate risks is to know what’s coming. This can be achieved with predictive analytics, a convergence of technologies aimed at identifying the most likely scenarios. As insurance businesses decide to invest more and more in data analytics, the advancements of predictive analytics are growing exponentially.
In this article, we’ll discuss how predictive analytics can reshape the insurance industry, what benefits you can expect from adopting predictive analytics modeling, and which use cases can be covered by the technology.
Predictive analytics in the insurance industry is a business intelligence technology that can determine the most probable outcomes based on patterns in data. Although predictive analytics is not a new concept, the emergence of big data, artificial intelligence, and machine learning has made the process simpler and more precise.
Not only is analyzing historical data manually difficult and time-consuming, but it’s become virtually impossible due to the ever-growing volumes of data. Every day, netizens and businesses generate immense amounts of data that need to be collected, manipulated, analyzed, and stored. Predictive analytics for insurance automates these processes making the analysis faster and more accessible.
From anticipating fraud and estimating financial risks to optimizing business processes and identifying potential markets, the insurance industry can derive endless benefits from data analytics. Using machine learning and deep learning algorithms, companies can predict customer retention and churn, determine human resources needs, identify cost-efficient opportunities for growth, and much more.
Before we dive into predictive analytics in insurance and its advantages and use cases, let’s explore how it works. From data collection and manipulation to model training and deployment, we’ll investigate how a predictive analytics model is built and validated.
Much like with any new endeavor, it’s crucial to determine the goals and objectives. Even before collecting data, define the deliverables of the analysis. For example, you may want to improve fraud detection, optimize your pricing, or increase customer satisfaction. Based on this, you can select the appropriate dataset to analyze and discover insights to solve your issue.
Once the objectives are set, identify metrics and KPIs to measure the performance of your experiments. Without clear and measurable benchmarks, evaluating the success of your predictive analytics model is nearly impossible.
The next step is extracting data from relevant sources for future analysis. Usually, companies acquire a data lake to store and manage data. They can deal with both structured and unstructured data and store it in a centralized location. Using AI-powered systems, this process can be fully automated, making it fast, error-free, and efficient. The data is pulled from numerous sources and includes internal data such as policies, claims, and customer data, and external data such as demographics, weather, and credit scores. Additionally, companies can utilize telemetric data and IoT-based solutions like smart homes and cars, wearables, etc.
In this stage, data science experts analyze the data to discover patterns and anomalies. This involves cleaning data to identify missing or duplicated data values and fixing errors. Performing exploratory data analysis allows specialists to comprehend data structure and establish relationships between data points. Additionally, data scientists combine data from different sources into a unified format to simplify and organize analysis steps.
After collecting data and determining what is being analyzed, you can begin to build a model. You can use statistical or machine learning algorithms for predictive data modeling — the technique can depend on the complexity of the model. From linear regression to complex neural networks, your data science specialists can choose the appropriate method.
Once the model is created, you can test it on a portion of the data to learn patterns and relationships. The remaining data is then used to validate the model and ensure its predictive analytics capabilities are on par with your expectations.
After successful validation, you can proceed to deploy the model in the real world. For example, you can integrate it with claims processing to see how well it does in fraud detection. This will allow you to reach the goals you set and help you develop new models to support other imminent issues.
Lastly, your data science team is in charge of continuous monitoring of the model to ensure its long-term validity. The process involves ongoing KPI tracking and adjustment whenever the model is underperforming. Moreover, monitoring allows you to insert new datasets and variables once they become available and relevant for analytics in insurance.
Predictive analytics can yield a wide range of advantages for the company and the individual insured. You can benefit from reduced costs, improved personalized experiences, identifying potential markets, and other capabilities of predictive analytics in insurance.
Among the most compelling reasons for investing in predictive analytics in insurance is cost savings. Automating and optimizing various repetitive and error-prove processes enables insurers to reduce operational expenses. From eliminating costly manual labor and setting accurate reserves to minimizing fraud-related financial risks, insurance companies can achieve substantial cost reductions.
Analyzing data associated with customer behavior patterns and characteristics opens the door to new markets and growth opportunities. Aside from user data, you can incorporate insurance industry trends, economic indicators, and competitor performance to uncover emerging market segments and audiences. Additionally, you can identify potential risks and gains of new markets to ensure the most profitable investment.
Historical data and customer interactions are the cornerstones of predicting the purchase intent. Based on customer behavior patterns, insurers can excel at identifying customers looking for a new provider. Once discovered, these findings can be used to offer motivators to improve their experience and convince them to stay with you.
It’s important to provide personalized offerings at every stage of the collaboration, including initial quoting in the claims process. The ability to predict when a customer may terminate the relationship with your organization gives you an opportunity to reduce churn.
However, nowadays, experts are talking about so-called hyper-personalization which includes understanding the customer and their needs, application of the latest technologies, and adopting a customer-centric marketing strategy. In more detail, hyper-personalization requires you to collect customer data from every interaction with the company, including mobile applications, social media posts, customer review platforms, and your websites.
Using predictive analytics in insurance, you can leverage data to build long-lasting relationships, improve customer satisfaction, and stand out in the market.
To improve customer engagement and make the experience more appealing and enjoyable, insurance companies focus on creating dynamic customer journeys. Using accumulated customer data, you can employ predictive routing to find the best agent for each customer, tailor the communication strategy to meet customer preferences, and implement AI chatbots to streamline query processing.
For example, the policy renewal process is a step that requires additional attention to avoid customer churn. Personalized messages and engaging interactions will increase the chances for a fruitful and successful continuation of the relationship.
The insurance industry has already reported some achievements brought by predictive analytics tools. Companies managed to cut expenses, increase sales, and boost overall profitability by 60%. This data showcases how predictive analytics in insurance can improve resource optimization and increase productivity. From accelerating insurance claims processing to enhancing customer engagement, the adoption of this technology yields significant benefits.
The insurance sector is incredibly competitive, making price optimization essential for success. By understanding the risks and analyzing market trends and customer behavior, you can develop a pricing model that attracts more customers. In conjunction with the aforementioned cost savings, you can create an appealing pricing structure that meets the preferences and needs of different segments.
With the ability to predict market fluctuations, customer traffic, and the effectiveness of marketing efforts, insurance companies can distribute their resources in the most efficient manner. Since your resources are limited, optimizing their use can significantly improve operational performance and profitability.
For instance, if you can forecast how many insurance claims are expected in this period, you can allocate your personnel accordingly. Not only does this help you save expenses, it also reduces processing time, thus increasing user satisfaction. Optimization of your marketing efforts can be done by identifying leads and focusing on the most promising ones to move them down the sales pipeline. This predictive ability allows you to reduce customer acquisition costs and increase conversion rates.
Predictive analytics in insurance enables proactive loss prevention to minimize waste. Instead of compensating policyholders reactively, companies can precisely identify risks and apply a preventive approach. Using vast amounts of sources like IoT sensors, social media, historical claim data, and more, you can anticipate financial risks almost flawlessly. Moreover, insurers can inform their clients about potential threats and offer mitigation strategies to avoid them.
Storing and analyzing historical data, market trends, and competitor performance can help you identify gaps in the market and develop new products to bridge them. For instance, IoT sensor data can reveal demand for a new type of home insurance product. Offering your services to secure the new product will give you a competitive advantage and help you establish yourself as an innovator.
Traditional actuarial analysis uses past claims to estimate reserves that cover future liabilities. However, this type of analysis only considers historical data without regarding emerging trends or unexpected occurrences. Predictive analytics in insurance offers a more dynamic approach and incorporates numerous data sources into the calculation. Using this method, insurers can identify precise reserves and avoid under or overestimation. Furthermore, you can conduct such analyses more frequently adjusting your reserves to the changing circumstances on the go.
Predictive analytics in the insurance industry is key to improving organization-wide decision-making. From day-to-day tasks to big-picture strategic functions, insurance companies can leverage predictive analytics to make data-driven decisions. As a result, agencies can be more agile and responsive in their efforts and allocate more time and resources to customers.
In this section, we’ll go over various use cases of predictive analytics in insurance that can be applied to your company.
Predictive analytics in insurance can empower underwriting processes through automation and precision. While traditional risk assessment is a time-consuming manual task that takes into account a few basic parameters, predictive analytics offers more advanced data insights. This allows insurers to develop a more accurate policy for each client without disregarding vital factors.
Furthermore, automated analytics in insurance enables companies to deliver results faster. Instead of taking days to calculate a quote, insurance agencies can generate it within minutes. Not only does this remove the burden from the employees, but also cuts underwriting expenses and enhances customer experience. Moreover, your employees will no longer need to perform mundane tasks and can focus on their core duties.
Insurance fraud is a major point of contention as it has grown so much that it has amassed 1.5% of the US GDP. According to FBI estimates, the average household pays an additional $400 to $700 per year because of insurance fraud. This worrying number makes the topic of fraud detection and prevention incredibly important.
Luckily, predictive analytics delivers models that can identify fraudulent behavior and alert the person in charge. After training on real-life cases, predictive analytics models can detect even subtle and sophisticated schemes.
For instance, insurers can train their models to monitor clients’ social media activity, third parties involved, and other participants to ensure the claim is legitimate. This is often used in life insurance claims. Insurance predictive modeling allows insurers to investigate the risk factors and monitor online presence to establish the person’s health.
Insurance claims processing used to take weeks and sometimes months encompassing data gathering, determining the events of the claim, cost calculations, etc. Predictive analytics in insurance allows companies to analyze myriads of data extremely quickly and efficiently. For example artificial intelligence and machine learning algorithms can swiftly identify complex claims and match them with the most seasoned experts. Meanwhile, simpler cases can be handled by an automation solution.
On top of that, predictive analytics tools can deliver the personalization that modern customers seek. User segmentation enables a more precise and detailed analysis of each user group allowing companies to develop customized policies that meet customer preferences.
Another common concern associated with the claims process is workers’ compensation. These cases can be quite complex and expensive, requiring additional human resources and expertise. Historical data like treatment plans and injury types can help models discover high-cost claims and prioritize them. For example, if a worker’s injury tends to cause chronic pain or disability, an insurer can cooperate with the medical provider to find the best plan of action.
Finally, every insurance company strives to minimize or even prevent claims altogether. This can be achieved with predictive analytics in insurance by aiding policyholders in avoiding events that can lead to a claim. Using IoT connectors, insurers can track various metrics and forecast if and when a certain piece of equipment may malfunction. Predictive maintenance will save the customer money and time in managing repairs or new purchases as well as protect the insurer from high claims costs.
The 360-degree view of customers is created by accumulating data from multiple touch points of the customer journey. Using predictive analytics in insurance, you can aggregate data from numerous sources and learn the behavior and patterns of each customer. From their buying habits and risk profile to insurance needs, you can generate a detailed profile for each client.
For instance, if you have a segment of tech-savvy Gen Z customers who prefer to communicate online, you can mold your strategies to appeal to this particular persona. Having comprehensive profiles also benefits cross-selling and up-selling tactics, allowing you to predict which client is likely to sign up for which offers.
Predictive analytics tools can also be of help when tackling customer churn. Monitoring customer behavior and tracking their online reviews and other activities gives you a chance to adjust your policy and retain the client. As it costs seven to nine times more to retain an existing customer than to acquire a new one, preventing churn should be at the top of your priorities.
Usage-based insurance in conjunction with telematics and IoT can revolutionize the car insurance industry. Insurance predictive modeling can track driving behavior and reevaluate the coverage plan accordingly. For example, while careful drivers can enjoy lower premiums, risky drivers will have to pay higher fees. Aside from delivering a more flexible pricing structure, this method encourages safe and cautious driving habits.
Telematics can also be employed to reenact an accident in case of a disputed claim, enhance the detection of fraudulent claims, and deliver more advanced data insights for insurers. With the expansion of IoT, the capabilities of predictive analytics in insurance will also grow. Companies will be able to leverage telematics in home or even health insurance plans.
Capturing and analyzing health data, including medical records, genetic information, and lifestyle patterns, predictive analytics in insurance can detect policyholders with high risks of developing certain health complications. To mitigate the risks, insurers can offer wellness, early intervention, and disease management programs.
In life insurance, companies are leveraging predictive analytics to include more factors in their analysis. Based on these findings, insurers can offer health programs to reduce mortality risks and prolong their customers’ lives and well-being.
Catastrophes are rare but when they do occur, they require substantial additions to your resources. Predictive analytics can consider historical data, monitor weather conditions, track seismic activities, and other vital factors to estimate the likelihood of a potential force majeure. Besides helping insurers understand risk exposure in their area, this analysis allows agencies to develop emergency plans.
In case of an actual catastrophic event taking place, you’ll be prepared to allocate resources to process claims and offer customer support faster and more efficiently. Moreover, more advanced data analytics enables insurers to simulate the potential impact of various scenarios to calculate the outcomes and repercussions.
Subrogation refers to seeking reimbursement from a third party after satisfying the policyholder’s claim. The process can be optimized by proactively identifying claims with a high risk of subrogation. Historical claims data allows predictive analytics models to detect such claims and initiate the process more quickly. Instead of manually reminding the third party of the payment and making calculations, you can automate the entire workflow and improve efficiency.
Litigation is a big concern for every insurance company. The ability to predict which claims are likely to lead to a lawsuit and mitigate the factors beforehand will save you money and sometimes even reputation. Analyzing past litigation data will offer actionable insights into the main characteristics of such claims.
Using this information, you can engage in early settlement negotiations or delegate the claims process to a special litigation team. Additionally, predictive analytics tools can estimate the likely duration of the lawsuit process and its outcomes allowing the insurer to prepare financially and legally.
Especially nowadays, supply chain disruptions cause a plethora of obstacles for businesses. Predictive analytics can aid insurers in underwriting business interruption policies more efficiently. The combination of factors like suppliers, geopolitical risks, weather conditions, logistics patterns, and other vulnerabilities, creates a holistic overview of the insured’s profile.
Based on this data, you can calculate the potential impact of different scenarios and develop policies that take them into account. You can even use a predictive analytics model to monitor the supply chain in real-time and better manage emerging risks and threats.
Similarly to car insurance, equipment and machinery can also be equipped with IoT sensors to monitor their health. Predictive analytics can track a variety of parameters, including temperature, humidity, pressure, and others to make accurate forecasts about the condition of the insured’s equipment. Proactive maintenance of the equipment will extend its lifespan and also minimize the potential claims costs for the insurer.
A reinsurer is a company that insures other insurance companies. In case of a major claims event, an insurance firm will be able to rely on its reinsurance partner to protect itself from significant expenses. Analyzing historical data gives insights into the company’s future losses under various scenarios. Using this data, you can determine the middle ground between risk transfer and cost efficiency.
Additionally, predictive analytics tools can aid you in assessing the potential reinsurance partner. You can evaluate their credit risks and overall performance and reputation to determine whether the agency is reliable and trustworthy enough. Deriving actionable insights will help you enhance your capital management, minimize risk probability, and guarantee financial resilience.
Although cyber insurance is a relatively new concept, it’s among the fastest-growing segments in the market. As the risks and severity of cyber attacks skyrocket, more and more companies are interested in protecting their assets from cyber crime. However, cyber risks are highly complex and pose a real challenge to underwriters.
Predictive analytics can help insurers evaluate the risks by analyzing data regarding the insured’s industry, IT infrastructure, security protocols, and past incident history. Additionally, the model collects data about the current state of cyber security, standard preventive measures, and common attacks. Predictive modeling in insurance can aid you in identifying risk factors and choosing the most appropriate policy to satisfy the insurer and the client.
Predictive analytics is a growing field that will only continue to improve with the advancements of artificial intelligence, machine learning, and deep learning. For example, claims processing can be fully automated with just a slight improvement of AI capabilities. From the first notice of loss to the final agreement, AI-powered models will be capable of autonomously executing the entire process.
Future developments of IoT and real-time streams can deliver continuous monitoring capabilities to insurance firms. As a result, premiums will become dynamic and will change depending on the ongoing risk evaluation. This can be expanded with behavior-based insurance plans. Predictive analytics models will determine the costs based on the individual’s behavior patterns as opposed to risk factors. Besides offering a more cost-effective solution, this approach also encourages positive habits.
Other important trends of predictive analytics include prescriptive analytics that also suggest the best course of action to minimize the risks. Furthermore, the future development of artificial general intelligence (AGI) may handle the vast majority of insurance operations.
Finally, it’s important to mention Analytics 3.0 which is the latest stage of maturity that encompasses big data, AI, machine learning, data mining, blockchain, and other cutting-edge technologies. In this new paradigm, the focus is on making AI systems more interoperable and explainable, meaning developing models that can communicate with other systems and explain how they came to a certain decision.
Insurance companies are tasked with endless analyses, decisions, and risk calculations, making the business extremely difficult. Delegating at least some part of it to an intelligent system can enhance your productivity, minimize bad scenarios, and protect your finances. Consider investing in predictive analytics to take control over risk factors and have the capability to see into the future.
NIX is a reliable IT partner who will guide you through the process of creating a predictive analytics model and integrating it into your current infrastructure. We are a software development company with decades of experience and a customer-centric approach. Get in touch with our experts to discuss your needs and utilize your data for valuable insights.
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