Like all other industries, the insurance industry is undergoing massive digital transformation powered by emerging technologies and innovation. Technology is empowering insurance businesses worldwide to improve their operations and offerings. Even though modern-day enterprises employ various tools and technologies, predictive analytics in insurance can be a major game-changer. For instance, it allows insurers to reduce risks related to fraud and make strategic decisions based on data-driven insights. In this blog, we will discuss the market size and drivers of predictive analytics along with its use cases in the insurance industry.
Predictive Analytics in Insurance Industry
The global insurance analytics market size was valued at USD 8.8 Billion in 2020. It is expected to reach USD 20.6 Billion by the year 2026, at a CAGR of 15.1 per cent during the forecast period. A number of factors such as the increased adoption of Artificial Intelligence, Machine Learning, and growing investments from InsurTech players are driving the growth of predictive analytics in insurance. The growing focus on digital transformation and Big Data are also major driving factors behind the rise of predictive analytics. It enables the insurance industry to build better models and maximize revenue and operational efficiency.
Popular Use Cases of Predictive Analytics in Insurance
As evident from above, the application of data analytics and related technologies like Big Data and predictive modelling in insurance has been growing fast. The demand for the technology is expected to surge exponentially in the coming years. The list of applications of predictive analytics in the industry is endless. Here are some popular use cases for predictive analysis in insurance:
Claims Segmentation
The value of personalization when serving customers cannot be magnified. By offering tailored services and products, insurance businesses can make the customers happier and more satisfied. With the help of a good predictive analytics tool, insurance agencies can know which claim to focus on first. Businesses can thus fast track the simpler insurance claims, and assign the more complex ones to more experienced claims managers. This helps insurance agencies significantly bring down the turnaround time for claims. By ensuring proper claims segmentation, predictive analytics allows effective assignment and settlement of claims. Moreover, predictive modelling techniques can also be used for advanced valuation and operational marketing.
Fraud and Risk Management
Insurance fraud affects everyone. It is a massive issue for both insurers and customers. For instance, claims fraud costs insurance businesses money and then they have to recover the losses via higher insurance premiums. The cost of detecting and investigating fraud can also be high. Outdated technology tools and fraud detection processes are slow, cumbersome, and often ineffective. Predictive analytics in insurance can be leveraged to identify hidden patterns and trends. The fraud analytics tools leverage structured, unstructured, and semi-structured information from an array of different sources. This data is then analyzed for detecting and preventing fraud. It also assists insurance businesses in taking proactive decisions to safeguard themselves against fraudulent insurance claims and misuse of annuities.
Customer Experience
Predictive analytics not just revolutionizes the customer-insurer relationship, but also boosts sales while simultaneously heightening customer satisfaction. And, satisfied customers lead to more business and increased loyalty. In the cutthroat insurance world, data analytics offers insurers a powerful competitive edge. It helps them make accurate predictions about customer behaviours based on historical data and feedback. By harnessing predictive analytics in insurance, agencies can boost customer experience, and maximize business value. Predictive analysis can help insurers understand and predict the needs of customers and customize solutions to drive customer retention and earn their loyalty. Insurance agencies can leverage data-driven insights to modify their existing processes or policies to suit the individual needs of their prospective and existing customers.
Steps for Adopting Predictive Analytics
In order to transform raw data into actionable insights and predictions, businesses must follow a logical process. To ensure the smooth adoption of predictive analytics, insurance companies can follow these steps:
- Setting Clear Goals
Starting a new project, especially one that is highly dependent on data, can be tricky. Leaders across the agency must take the initiative for setting goals that contribute to the project’s execution. They must make a list of all the metrics that are valuable to the company. By setting fixed goals, businesses can easily outline the gaps to target.
- Conducting Market Research
Market research is essential while implementing predictive analytics in insurance. Businesses must first make a list of all the pros and cons of the undertaking and then assess how it will affect the business operations. A number of InsurTech players offer predictive analytics solutions and services. Businesses can avail their services to ensure the smooth adoption of predictive analytics.
- Handling and Cleansing Data
Data fuels predictive analysis. The right data can steer users on the right path. Therefore, businesses must focus on capturing data from an array of sources, including their enterprise software, forms, customer feedback, social media, etc. While the quantity of data is important, the quality should not be undermined as well. Businesses must cleanse the data and ensure it is easily accessible and ready to be used for predictive modeling in insurance.
- Fostering User Engagement
Adopting predictive analytics involves a huge investment. Businesses put in significant effort and money to help employees boost productivity, but what if the employees are not making the best out of the technology? It leads to inefficiencies and negative ROI. Businesses must help the end-users embrace the new tech. Effective user adoption will go a long way in ensuring higher returns from the investment.
Conclusion
Today, insurance agencies make regular use of predictive analytics for analyzing target consumers and improving retention. It also helps in preventing churn, improving customer lifetime value, fraud detection, predictive maintenance, etc. With advanced analytics tools, insurance agencies can also manage and assess risks more accurately, thereby improving profitability. Boosting widespread adoption of the technology throughout the organization will benefit insurers immensely.
