The advancement of insurance technology (Insurtech) is leading to rapid evolution in the industry. Spearheading the disruption is the adoption of machine learning, powering predictive analytics and leading to improved efficiency, and streamlined operations.
“Established insurance companies aren’t confronting the fact that they need to become technology companies” – Leon Gauhman, Chief Product and Strategy Officer for Elsewhen
Using data to generate predictions and create forecasts is not new in the insurance industry, however, the advancement of new technology has led to previous manual methods being replaced by machine learning algorithms.
Insurance companies have access to an incredibly large volume of high-quality data, and machine learning-powered predictive analytics has unlocked a multitude of defined use cases that are increasing efficiency and directly impacting the bottom line.
Fast movers in the industry have already begun adopting the technology, and stiff competition means the role of predictive analytics in insurance is expected to grow rapidly in the coming years, with the global insurance analytics market expanding at 15.1% CAGR by 2026.
Predictive analytics is a branch of data analytics, where most commonly machine-learning models are used to predict future outcomes, based on patterns in historical data, and provide companies with a way to make decisions based on data that is collected.
The potential use cases of predictive analytics depend on the type of business that utilizing it and the data that they have access to. For the insurance industry, many use cases focus on predicting customer behaviors to more accurately represent risk and maximize profitability.
Traditional policy pricing was inflexible, with plans based on group-level characteristics. With predictive analytics, insurers can capitalize on new types of customer-specific data available, to provide dynamic policy pricing, tailored to the customer's needs. Models can analyze this data to provide personalized policy pricing, mitigating risk for insurers and flexible, fair pricing for customers.
Insurance fraud is a major cost for insurers, costing at least $80 billion per year. The traditional methods applied on such a large amount of transactional data make detecting suspicious claims difficult, leading to as much as an estimated 10% of all claims being fraudulent each year. Predictive analytics provides a way to analyze a large amount of customer data, and flag outlier claims that indicate a possibility of a fraudulent claim.
Retaining customers is vital for insurance companies to stay profitable, however, increased competition, lower switching costs, and greater access to information than ever means that consumers are canceling or not renewing their policies more than ever. Predictive analytics allows insurers to build machine learning models that analyze customer behavior to predict their likelihood of churning in the future. This information allows retention teams to engage with these customers, and incentivize them to renew with competitive offers.
Following the previous point, increasing overall customer satisfaction is another way to increase customer loyalty and reduce churn. One effective way to do this is by delivering a highly personalized experience. Predictive analytics allows companies to segment their customers by their attributes and predicted behaviors, meaning they can personalize communication and deliver offers that resonate.
Predictive analytics can provide a wide range of compelling benefits for insurance companies that adopt the technology.
Customer relationship management is of utmost importance to any insurance company and can result in direct benefits such as greater retention, higher cross-sells, and increased market reputation. Predictive analytics provides multiple ways to meet individual customers' expectations, through personalized communication, delivering relevant offers, and greater efficiency in claims processing.
Predictive analytics is impacting the bottom line of insurance companies that have adopted it through direct cost savings. Whether this is through a reduced amount of fraud, a reduced rate of churn, or more accurately representing the risk of a policy, the potential cost savings from the implementation of predictive analytics is enormous.
Using machine learning over manual statistical methods to power predictive analytics has a significant advantage in speed. Machine learning automates, and streamlines time-consuming data analysis involved in assessing the risk profile of a policy applicant.
Although taking advantage of predictive analytics can bring extensive benefits, companies must prepare in advance to reduce the challenges that arise in implementation.
When insurers make decisions based on the predictions from AI and machine learning models, there is the chance those models are unfairly biased, presenting an ethical dilemma for insurers. Consumers and regulatory bodies will demand adherence to ethical AI practices and guidelines, requiring transparent models that provide equitable results that can be understood and validated.
Implementing predictive analytics can be difficult without the right data infrastructure and expertise because predictive analytics relies so heavily on high-quality data. A company that has a fragmented data management system may encounter obstacles when implementing predictive analytics into day-to-day business operations.
Although predictive analytics has already gained significant traction in the insurance industry, the technology will continue to mature. As this happens, new trends will emerge.
The cost and expertise barrier to entry for predictive analytics will be reduced, and insurance companies won’t have to rely on large data teams to build predictive models. No-code tools such as the AI & Analytics Engine will allow professionals in the insurance industry to connect directly to their data and build predictive models without assistance.
There will be an increase in the volume and variety of data that will be captured and used to train predictive models. Alternative data sources from wearables, connected devices, or social media will allow for more accurate representations of risk, and new sources of data will provide insights that were unable to be captured previously.
Hopefully, after you’ve read this you have a better understanding of why and how predictive analytics is being used in the insurance industry. If you’re curious about how it’s being used in other industries, check out our other blogs on:
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