As the insurance market gets more competitive, insurers are using technology to improve service offerings. As of tomorrow, insurance will be digital, or it will not exist. Technology is especially effective in the insurance industry for attracting millennials and Gen Z customers who are more tech-savvy compared to the earlier generations.

—————————————————————————————————————————————————–

ALSO READ: Top 8 Technology Trends That Will Drive Growth in the Insurance Industry

—————————————————————————————————————————————————–

With the advent of technology, there is availability of data and as everyone is aware, the insurance industry thrives on this data. It is based on a simple equation i.e. better data leads to more accurate risk calculations which, in turn, leads to higher margins. Thus, data analytics plays an important role in insuretech.

Before we delve further into this, let us first understand what is data analytics?

Data turns into a strategic asset when you can actually put it to work. Data analysis is a process of inspecting, cleansing, transforming, and modelling data with the goal of discovering useful information, inferring conclusions, and supporting decision-making. IDC Data Age predicts that by 2025, the total amount of digital data created worldwide would rise to 163 zettabytes.

How Does Data Analytics Benefit Insurers?

Data analytics tools can now collect data from a variety of sources – both internal and external – to better understand and predict the behavior of the insured. This leads to several benefits, some of which are listed here:

1. Lead Generation: Unstructured data available on the web is a valuable source of lead generation. Data analytics of such unstructured data provides insurance companies with key insights into the customer behavior and market opportunities to up-sell and cross-sell.
2. Enhance Customer Satisfaction: Using predictive analytics, insurers can quickly and accurately consolidate data and generate new insights regarding their buying habits and understand the needs/desires.
3. Claims Prediction: Predicting the turn of events in the future is of paramount interest to the insurance industry. Being able to make accurate claim predictions helps to mitigate risks, gain competitive advantage, and reduce financial losses.
4. Reduce Fraudulent Claims: Frauds are common in the insurance industry. Thanks to data analytics, there are ways to reduce the attempts of fraud by a considerable extent as the insurance companies can make use of the actionable data intelligence to figure out who may be a likely fraudster even before the fraud happens.
5. Predicting Accurate Risk for Underwriting: Underwriting is a complex task for the insurers which can be simplified through data analytics. For example, the data trend coupled with telematics helps to predict a higher premium for a customer with rough driving pattern as compared to the customer with relatively safe driving pattern.
6. Improve Claims’ Process: There are many customers who may complain that claim settlements often take a long time as there is a lot of analysis that needs to be done by the insurers. This process can be simplified with the help of data analytics leading to faster settlement, which means that customer satisfaction will also greatly increase.
7. Provide Personalized Experience: Just like most other industries, personalization holds the key in the insurance industry as well. Customers are willing to avail services that best suit their needs and lifestyle and look for personalized offers, policies, loyalty programs, and recommendations. Data analytics provides insurers with valuable insights such as demographics, preferences, lifestyle details, interests etc., which enables them to provide highly personalized and most appropriate experiences.
8. Accurate Pricing of Premiums: One of the major challenges faced by the insurance industry is to accurately calculate the premiums for each policyholder. By deriving actionable insights from data analytics and tracking individual policyholders’ behavior, the prices of the premiums can be set.
9. Customer Lifetime Value (CLV) Prediction: CLV is predicted using customer behavior data which helps to determine the customer’s profitability for the company. Such data gives insights on the likelihood of customers’ behavior in maintenance or surrendering of a policy. It can also be leveraged for developing market strategies.
10. Self-servicing of Policies: Insurers are offering a portal for policyholders to manage their own policies which takes a lot of work off their plate and makes customers happier as well. Such portal helps insurers to automate the process of making smart recommendations to customers right when they are looking for a new policy or making changes to an existing one.

Wrapping up

Having good data is one thing; knowing how to maximize its usefulness is something else entirely. In recent years, as insurers have sought to become more relevant to their customers, they have realised the strategic importance of their data investments. Predictive modelling and big data have become the insurance industry powerhouses. Data-driven organizations are more likely to acquire new customers than their peers which is why going forward, more and more insurers will use data analytics to help forecast events and gain actionable insights into all aspects of their businesses.