The rise of Fintech, changing consumer behavior, and advanced technologies are all disrupting the financial services industry, including its most visible member, insurance. The insurance industry is poised for a digital transformation of how it conducts business, with analytics capabilities at the forefront.
The use of data analytics in the insurance industry is growing all the time.
According to the latest report, insurance businesses are expected to invest up to the US $56.97 billion by the end of 2022. Research shows that deployment of data leads to 30 percent improved access to insurance services, 40-70 percent cost reductions, and 60 percent higher fraud detection rates, all of which benefit customers as well as stakeholders. We can safely say that analytics has transformed the insurance industry for the better. Its ramifications have allowed insurers to better target customers and address their insurance needs.
What are the challenges of the Insurance Industry?
The data on insurers has enabled customers to find the best product commensurate with their requirements. The easy access to information has raised the bar for insurance companies.
As a result, companies require to better prepare to address the requirement of their customers along with mitigating the insurance risk as a result of increased competitiveness in the domain.
Apart from the competition, the information asymmetry, and alteration of documentary evidence are posing fresh challenges to insurers. Leveraging the combination of machine learning and technology solutions can detect such frauds considerably and has proved to be a valuable tool to mitigate such risks. Apart from customized products and fraud detection, data-driven insights can help drive profits and better understand customers.
Data analytics can also help in generating the risk profile of the existing customers. It can help companies to better determine who to upsell and cross-sell the allied products leading to raising the customer lifetime value of the existing policyholders.
Role Of Analytics In The Insurance Industry
To target clients, the insurance sector has long relied on data analytics. Statistics are used by a variety of insurance firms to segment their consumers, including travel insurance companies, health and life insurance companies, property and casualty insurance companies, and so on. Accident statistics, policyholder personal information, and third-party sources all assist in categorizing people into distinct risk groups, preventing fraud losses, and reducing costs.
The move to digital platforms has opened up new sources of data that may be utilized to decipher a customer’s complicated behavioral patterns and precisely identify his or her risk segment. The data can thus be used to affect insurance underwriting, rating, pricing, forms, marketing, and claims management in the insurance industry.
1. Customer acquisition
Everyone generates a large amount of data through all their internet activity. This unstructured data can be acquired to analyze customer behavior not just online but offline as well.
Insurance businesses can design tailored marketing strategies to acquire new consumers by analyzing unstructured data. It helps Insurance companies to devise their marketing strategies.
2. Retention of Customers
No one wants to lose customers. If a company’s client retention rate is high, it is deemed successful. The insurance business is no exception. As a result, insurance companies use data analytics to take extra care of clients who might otherwise quit.
Based on user activity, AI can identify early symptoms of customer unhappiness. Insurance companies can rapidly respond to the information provided to enhance their services and find a solution to the customer’s problems. Insurers might offer discounts to clients or even change their pricing strategy.
3. Fraud Detection and Prevention
Insurance companies in the United States lose more than $80 billion each year due to fraud. Such deception results in higher premiums for all stakeholders.
Data analytics can be used to protect insurance companies from such fraud. By using predictive analytics, insurers can compare a person’s data to previous fraudulent profiles and identify cases that require further investigation.
4. Risk Evaluation
The entire concept of insurance companies revolves around risk diversification. While assessing risks, insurers have always prioritized the verification of customer information. Based on their data, customers are classified into various risk classes.
Big data technology has the potential to improve the overall efficiency of the risk assessment process. Before making a final decision, an insurance company can use big data and predictive modeling to anticipate potential issues based on client data and classify them accordingly.
5. Cost Saving Using Insurance Analytics
One of the many advantages of leveraging technology is cost reduction. Machines’ increased role in the industry increases efficiency, which eventually leads to cost savings.
Big data technology can be used to automate manual processes, increasing their efficiency and lowering the costs associated with handling claims and administration. This will enable the companies to offer lower premiums to their customers and thus stand out in a competitive market.
6. Customized Policy Offerings
We all want to be treated with respect. Companies have recognized the importance of providing a personalized experience. Unstructured data analysis can assist businesses in providing services that are tailored to and meet the needs of their customers.
For example, life insurance based on big data can become more personalized by taking into account a customer’s medical history as well as the habits detected by activity trackers. The data can also be used to determine a pricing model within the client’s budget and profitable for the company.
7. Insurance Underwriting Automation
Data and Advanced Analytics can help the insurance industry save time and resources involved in insurance underwriting and automate the process. Thanks to big data technology, insurers can work quickly on a customer’s profile. They can review their history, select an appropriate risk class, develop a pricing model, automate claims processing, and provide the best services.
8. Generating More Leads
Insurance data analytics of such unstructured data gives you a deep dive into customer behavior and market opportunities for upselling and cross-selling.
CRM and agency management systems, for example, use data analytics to extract valuable insights from reports that reveal the customer journey from search to conversion. It assists them in understanding customer behavior and allows the marketing department to target the appropriate messages for warm-up leads.
The insurance industry’s data is growing at a rapid pace, with a 90 percent growth in the last two years. Data analysis is used by insurance companies to better determine the price of policies, settle claims, study consumer behavior to facilitate benefit distributions, detect fraud, and map dangers, among other things.
Data analytics can handle the volume, velocity, and variety of data in ways that earlier systems can’t. Lowering processing costs, boosting pricing, competing with major enterprises, focusing on local preferences, and enhancing efficiency, sales, and customer loyalty are all advantages of data analytics. Thus analytics has an impact on every area of the insurance and fintech industries.
Virtue’s insurance data analytics solutions assist our insurance customers in capturing and integrating all available internal and external data to create a single source of truth, resulting in powerful competitive advantages.
Data analytics is not only revolutionizing the insurance industry but is also redefining other industries—especially retail. To know more, read the blog on analytics in the retail industry.