Leveraging Insurance Analytics for More Strategic Value

Smart, digital solutions are driving results across all value chains of various industries. And the insurance sector is also witnessing this overhaul.

As data becomes more ubiquitous, it is laying the foundation for a data-based ecosystem that can source insights from data – be it from customers, agencies, or even assets. However, such a framework of extracting meaning from data is only possible when you have data analytics systems in place. It allows you to process, handle, and work with data at all ends. And this is where insurance analytics enters the picture. Let us review the role of insurance analytic in delivering value.

The Business Value of Insurance Analytics

The global market of insurance analytics was valued at USD 8.8 Billion in 2020 and it is expected to reach USD 20.6 Billion by the year 2026, growing at a compound annual growth rate of 15.1 percent. The business value of insurance analytics is rooted in its capability to overhaul business processes. While productivity, speed to market, and efficiency reside at the heart of profitability, actionable insights generated from insurance data analytics are the secret weapon.

Smart Data Analytics in the Insurance World:
Use Cases

Given its multivariate use, insurance data analytics can be applied to various aspects of business operations and processes. To demonstrate this, the following are some use cases of data analytics in insurance:

Fraud Detection

Fraud detection can be an arduous process for insurance companies as essential data is held in silos. Insurance analytics can deliver data-driven insights to help insurance companies detect and prevent fraudulent insurance claims and provider payment abuse. Harnessing the power and potential of predictive modeling, social media analytics, and big data across different states of a policy lifecycle empowers insurance companies to effectively and efficiently mitigate fraud. By analyzing past data, trends, and variables, claims history, etc, insurance data analytics tools can establish patterns and calculate the probability of frauds.

Real-time Risk Analysis

Insurance analytics can help insurance companies carry out risk analysis in real-time. This facility, in turn, will enable them to become highly responsible in a highly volatile environment driven by internal or external stimuli.

For instance, connected automobiles can continually send a number of data points to servers every second. Insurance companies can get exact details of the location of the vehicles, their speed, braking, steering, and acceleration in real-time. Insurance companies can share real-time updates on the state and condition of the route so that drivers can make calculated decisions on whether or not they should follow the path.

Insurance Underwriting

Processing insurance applications and establishing pricing for accepted insurable risks can be an arduous process. Underwriters have to go over volumes of data troves for determining the risk of a potential policyholder. When done manually or using legacy systems, the underwriting process can be extremely sluggish. Insurance analytics paired with artificial intelligence can streamline and hasten the process to a great extent. It leaves ample room for underwriters to focus on high-value tasks that generate revenue and expand the business. All in all, insurance data analytics can build an environment that is highly conducive to growth and increased efficiency.

Concluding Thoughts

Owing to the rapid advancements in technology and increasing usage of inter-connected devices, insurance companies now have unprecedented access to large volumes of customer data. Against such a background of brimming data reserves, it would only make sense to harness these assets to accelerate business growth. So whether you seek ways to streamline your business or to increase sales, you will find all your answers through insurance analytics.