Every business process today is being transformed with the help of technology in order to make it more refined and valuable. Data analytics is one such technology that is making business models and processes streamlined using valuable information collected from different sources.
From manufacturing companies to hospitality ones, every sector is leveraging data analytics’ opportunities to understand the needs of customers better and serve them accordingly.
Talking about the insurance industry, it too is leaving no stone unturned when it comes to making most of the data-driven opportunities available to them now because of data analytics for strategic decision making. So, let us dig deeper into how data analytics is changing the insurance industry at large.
Interesting Data Stats/Statements
- You may be astonished to know that almost 90% of the entire global data was formed in the last 2 years, and each day, around 2.5 quintillion bytes of information is created by the world.
- Bernard Marr (Business analyst and Forbes’ writer) explains, “Every interaction with your computer or phone creates data. Every interaction on social media creates data.
- Every time you walk down the street with a phone in your pocket, it’s tracking your location through GPS sensors– more data. Every time you buy something with your contactless debit card? Data. Every time you read an article online? Data. Every time you stream a song, movie or podcast? Data, data, data,”
- According to Fortune, by 2026, the global market of Big Data analytics will cross $1trn.
9 Ways Data Analytics is making Insurers Smarter
1. Premium Pricing
When it comes to the prices, there always exists tight competition between brands, and with the coming of digitization, it has intensified even further. But this does not mean that insurers need to compromise with their bottom line to win more clients.
With the help of data science technologies, they can build predictive models for processing historical data on claims, costs, profits, etc., in order to develop an intelligent pricing strategy. Also, it can also help you in adopting a pricing strategy that is clearly based on clients’ past claims, medical, and lifestyle history.
2. Pricing and Underwriting
The ability to underwrite (assessing risks in accordance with policies’ price) is what sets one insurance firm apart from others. Using detailed information from massive data sets, data analytics is providing insurers with an opportunity to offer insurance policies in compliance with the associated individual risks.
But insurers should also continue with their old analytical methods for even better risk assessment. As per the European Insurance and Occupational Pensions Authority (EIOPA) report, “traditional data sources such as demographic data or exposure data are increasingly combined (not replaced) with new sources like online media data or telematics data.
It is providing greater granularity and frequency of information about consumer’s characteristics, behavior and lifestyles.” So, firms need to use their years of experience and combine the same with the power and insights of data analytics to offer services that are both beneficial to the customers as well as to them.
3. Sales and Marketing
The sales and marketing process is crucial for any insurer in the present competitive business scenario, and using data analytics, they can take this process to the next level.
Today, insurance firms segment their customer base granularly according to lifestyle, age, behavior, income, etc., and work on devising targeted campaigns. Insurers can reach their customers timely using data-driven marketing automation.
Apart from this, recommendation engines can persuade and help your clients in choosing the best insurance policies based on their previous data. Valuable insights into Customer Lifetime Value (CLV) and customer churn can help you in building hands-on strategies, ensuring their loyalty in the long run.
4. Claims Prediction
Claims prediction holds immense importance in the working of an insurance firm. The reason being it helps insurers in building the best pricing models for their customers. A range of models and statistical tools have been used by insurers in the past during insurance claims management to predict claims.
However, the kind of accuracy that data analytics has given them in predicting claims is simply unmatchable. Now, insurers are able to process a vast volume of data that is gathered from multiple sources for building predictive pricing models and predicting claims like never before.
5. Insurance Fraud
The insurance industry witnesses maximum frauds, which lead to huge financial losses for insurers. According to insurance fraud.org, for insurance firms in the USA and Canada, fraudulent activities are reasons for 5-10% of the cost of claims. Also, approximately 32% of insurance owners stated fraud was as big as that 20% of the cost of claims.
With the help of predictive analytics models, advanced software can be built easily for processing large volumes of transactions and data for detecting suspicious fraud activities’ patterns. This will help insurers to identify and investigate suspicious cases to catch fraud.
6. Health and Wellbeing
Wearables that are IoT-powered have a huge role to play in personal health insurance. Just like telematic helps in monitoring vehicles and tracking customer behavior for rewarding safe driving habits, devices such as Apple Watch’s Health and Fitbit App can easily track and encourage healthy driving behavior, paving the way for lower premiums.
Data gathered during this person-to-person focused manner enables customization of insurance plans according to the health and behavior of a person. So, data analytics leads to better premium pricing when it comes to personal health insurance.
7. Enhanced Customer Experience
Quality customer experience is what your customers want, and insurers should leave no stone unturned in ensuring the same. For this, they need to provide customers with personalized solutions that are only possible with data analytics.
ML and AI can be used for building detailed customer profiles through tapping into external sets of data for scratching similarities in internal sets of data for producing a holistic customer profile.
If we talk about the call centers, AI can be used for analyzing the reason of call, the volume of the call, and specific calling times of specific customers.
After collecting this information, insurers can easily reduce the waiting time and ensure the availability of the right customer service executive at the right time, leading to an enhanced customer experience eventually.
8. Policy Developing and Tailored Insurance
Personalization is something that is fast becoming a norm amongst businesses around the world in order to win and retain customers, and this holds equally true in the insurance sector as well.
The Organisation for Economic Co-operation and Development states, “Insurance sets prices by groups of people who have similar risk profiles, whether, for example, by gender or age for auto insurance, which is called risk classification.
Big Data provides new sources of information for understanding policyholders, fine-tuning the risk classification.” So, all this leads to tailored policy offerings to the customers using relevant data.
9. Seamless User Interactions
Insurers can easily integrate social media with the customer portal in order to offer improved usability, along with collecting essential data points of customers.
For instance, an insurance company in Dutch, namely Kroodle has allowed its clients to interact with, use their Facebook credentials to login directly, and request the desired services, streamlining customer connectivity.
InsurTechs can also use the social media data for investigating fraud through comparison of claim records with the social media activity of customers.
Undeniably, data analytics opens doors of opportunities for insurers. They can not only streamline their business operations but also make effective business decisions and offer enhanced service quality to their customers.
So, if insurers need to leverage data analytics, they need to do it immediately as customers are already looking for personalized solutions from their carriers and it won’t be possible without the help of data analytics.
If they are not technically skilled, they can always opt for insurance business process outsourcing services, comprising highly qualified and skilled individuals for round-the-clock support.