In today’s time, it is imperative for every sector to focus not only on better data handling solutions, but also steadfast the use of the data that is long untamed. The findings of a study indicate that data-driven organizations are 23 times more likely to acquire customers than their peers.
Insurance is a data-driven industry. Every day there are new players in the competition and each one of them has a mine of data, but only the ones converting that data into useful insights can make it a gold mine. According to the findings of a recent study, 86% of insurance companies are working on Insurance data analytics mechanisms for optimum predictions of big data reports.
According to a predictive analytics report, current investment in predictive analytics of the Individual Life and Individual Health ecosystem is 70 percent and 40 percent respectively, which is assumed to grow up to 90 percent and 80 percent in just the next two years.
This is the power of data that is being used as a source of energy today. But this source of data needs to be unleashed to its full power by procuring insights that will help the insurance companies achieve their long-term goals.
If advanced analytics in insurance is leveraged appropriately, it can revolutionize the insurance business.
Use of Business Analytics in Insurance
How does Data Analytics Benefit Insurers?
Insurance companies using data analytics solutions have witnessed significant improvements in decision-making, business intelligence, customer conversion, etc. The key benefits offered by data analytics are:
1. Generating More Leads
In the age of competition, every insurer is facing difficulty competing with the internet. In this scenario, the unstructured data available on the web is an unchained source of lead generation. Insurance data analytics of such unstructured data provides you a deep dive into the customer behavior and market opportunities to up-sell and cross-sell.
Data analytics-enabled tools like CRM and agency management systems enable businesses to extract valuable insights from reports that reveal the customer journey, right from search to conversion. It helps them understand the customer behavior and enables the marketing department to target the right messages for warming up leads.
2. Improving Customer Satisfaction
Happy customers make happy businesses. Customer satisfaction is one of the greatest initiatives of advocacy, referral marketing, and brand identity creation. If a business is successful in fulfilling customer expectations, it will automatically register accelerated and unprecedented growth. According to a McKinsey report, satisfied policyholders are 80% more likely to opt for policy renewals.
An insurance company that can correctly predict the needs of the prospective customers by looking through data trends has much more potential to make the sale than an insurance company just using conventional methods of selling. Analysis of the existing customer data can also offer prescriptive insights in improving customer satisfaction.
Harness the Power of Data Analytics for Accelerated Business Advantages
3. Mitigating Claims Fraud
Claims fraud continues to be a major challenge in the insurance sector. However, insurance companies using data analytics have seen considerable improvements in their fraud detection process. With the application of data analytics, insurance claims fraud detection becomes speedier and more accurate. For example, the history of fraudulent cases is stored in the data trends of an insurance company and while processing any claim, the insurers can carefully check if the trend is repeated. This, in turn, helps reduce the act of fraud.
Apart from fraud detection, analytics can also be applied for fraud prevention and mitigation. Advanced analytics and claims predictive modeling leverage both business data and information from external sources for identifying potential claims fraud. Even before the submission of the claims, the predictive analytics model can detect individuals who have more odds for submitting fraudulent claim reports.
4. Predicting Accurate Risk for Underwriting
Underwriting is a complex task for insurers and it can be simplified through insurance underwriting analytics. For example, the data trend would predict a higher premium for a customer who has been engaged in rough driving than that of a customer whose data trend predicts a lesser risk profile.
The application of advanced analytics in insurance underwriting process encourages underwriters to concentrate on subjective tasks that call for judgment and intuitive decision-making while enabling systems to handle back-office work. Data analytics models can also be used for developing better underwriting rules. This, in turn, contributes to a uniform application of underwriting practices and lesser risks.
5. Enabling Business Growth
One of the important elements of the Insurance domain is quantifying the levels of risk so that it can accelerate business growth. Until recent times, the calculation of this business-critical risk was purely intuitive and largely guesswork. However, with hordes of data now readily available, it is possible to base such assessments on pure data than conjectures, and even predict eventualities that can disrupt operations. Accordingly, insurance agencies can analyze this data and plug revenue leakages that could be eating into the business’ profits. In this way, Insurance data analytics acts as an engine to the growth of Insurance companies with its capability in predictive analysis of big data.
The emerging leaders of the insurance sector are leveraging insurance data analytics while making decisions concerning pricing strategies and risk selection. The new-gen technology is working progressively for deploying prescriptive methods of procuring deep insights from the big data in various insurance-related transactions like underwriting, claims management, customer satisfaction, and policy administration to ensure better predictive analysis. This enables insurance companies to portray analytical decision-making in all their internal processes and business transactions.