How Big Data Analytics Helps the Insurance Industry to Serve Customers Better
When it comes to the insurance industry, the cut-throat competition always forces the companies to use innovative and deliberate ways to get a competitive edge over others. It is one of the most competitive markets. Furthermore, it is also a business sphere that is less productive and mostly functions on available data, statistics and information from various sources due to its sheer dependency on risk.
Traditionally, insurance companies had limited data and information to use for risk assessment, fraud claim findings, unique product offerings and to improve customer experiences by offering impeccable services. However, the scenario has changed dramatically after the introduction of Big Data.
First of all, we need to learn what Big Data is. By definition, Big Data is a number of datasets that cannot be assessed by traditional data management tools. These datasets are used to evaluate and access behavioral patterns, consumer preferences and other interactions to offer customized products and services accordingly.
Big Data technology collects data from various sources and evaluates with data management tools to see patterns, trends, and associations to come up with relevant offers for consumers. In the insurance industry, as per the 2015 research, 42% of executives from numerous insurance companies revealed that they use Big Data in offering prices, underwriting, and risk assessment. Also, notably, 77% of them said that they will use in the future to improve the overall business.
There are many ways Big Data can help insurance companies to assess risk, streamline different operations and improve customer experience by offering customized and personalized services. It is a golden opportunity for companies to get a competitive edge over others by implementing Big Data strategy with the help of Big Data Development Companies in their plan.
Here, in this post, we will examine various Big Data use cases which can help the insurance industry to come up with different customer-centric strategies and plans.
For example, a user is planning to buy a car insurance plan. Here, Big Data can help the insurance company, to a great extent. You can check out the user's vicinity and evaluate his past driving records for risk assessment. Not just that, Big Data also aids the company to decide the premium and insurance cover amount too. These actionable inputs also help the company to serve the customer conveniently.
With IoT and wearables, companies can get sound information about the insured person’s health and increase the cover and premium amount too.
Here, certain mobile apps too can help the insurance company to access valuable information about users. They can get relevant information such as health insurance, past records of buying healthcare devices and instruments. Such information enables the insurer to come up with the insurance plan that perfectly suits the customer.
Anyone associated with the insurance industry knows that insurance frauds are common. Out of 10 claims, 2 or 3 claims are a fraud. Also, every year, insurance companies make a huge loss due to such false claims. Thanks to the data science and fraud detection techniques, It has become easy to detect fraud claims.
Insurance companies have an algorithm that helps to detect false claims by constantly feeding the data about previous fraudulent activities and illegal patterns in the algorithm. Identifying such fraudulent activities is crucial for companies to lose money, sometimes, huge amounts.
Also Read: How Big Data And Cloud Computing Can Turn Things For The Businesses?
Price optimization offers various benefits to the customers and insurance companies too. Coming up with optimized prices for premiums can help to win customer loyalty and also improve revenues and profits.
Here too, different algorithms are used for price optimization. There are various factors considered before reaching the final price such as customer's price sensitivity, previous year's premium charges, expected losses, and costs and predicted risk criteria and previous claims.
All these elements are evaluated and analyzed to optimize the premiums that are most relevant to the current risks associated with the insured person. Though a few insurers believe it a not-so-trustable-practice, the reality is, most of the insurance companies use data for price optimization.
Prioritizing customers’ preferences is very crucial for any organization to excel. Big Data helps companies to serve this purpose effectively. After the introduction of Big Data technology in the insurance sector, companies can easily make or amend policies and products after evaluating and accessing data from multiple sources.
With such pivotal data in possession, companies can gain vital information about customers’ preferences, demands. They can check out the questions asked by the customers and also help them to find appropriate answers to improve overall customer experience by offering such personalized products and services.
Offering personalized services is the sure-shot strategy to win the customer’s loyalty. The beauty of this strategy is that it never fails you, irrespective of the business spheres you are in. When you take care of it while proposing the insurance plan to the customer, it will surely give you an upper hand.
With the Big Data technology, insurance companies can make this happen as they have a large chunk of data about customer preferences, interactions, lifestyle patterns, previous buying patterns, interests, hobbies, and demographic preferences. With such data to assist them with, they can easily come up with personalized policies, custom offers, loyalty programs and different payment options as per customers' preferences.
Along with that, consistent communication efforts with the customers about new offerings, latest schemes and premium payment reminders too help to build a personal relationship with customers.
For any organization, evaluating the Customer Lifetime Value (CLV) is momentous. By definition, CLV is a simple calculation of revenues gained from the customer minus expenses made. However, it gives you insight about the critical future relationship scenario with the customer.
If the CLV is higher, you should certainly invest some time to ensure that the customer is happy and satisfied. The CLV prediction is carried out by different behavior models and various factors are taken into consideration to get a clear idea about the possible income in the future. Frequency, monetary value, and recency are some of the factors that help the company to predict future income. With enormous data on your hand, you can easily predict the CLV value by making algorithms at work.
Risk assessment is one of the most important things to determine by insurance companies and Big Data is playing a big role in it. Risk assessment helps companies to decide the policy premiums and insurance coverage amount. There are telematics, IoT devices, and wearables that help companies to assess the risk.
There are various factors taken into consideration such as road conditions, neighborhoods, drivers' past records, and many more. Predictive analysis modeling is used by insurers to predict the risk.
. In the life insurance sector, similarly, with the wearable devices and activity tracking mobile apps, companies can keep track on a user's activities and habits. Even some insurance companies also send personal texts to users about their possible health issues and conditions. Certainly, Big Data has revolutionized the way insurance companies assess risk.
Conclusion:
Big Data has given a paradigm shift to the way companies use data to offer more customized services and ultimately, to improve user experiences. The insurance industry that is completely dependent on data possession and management has gained much with such a large amount of data. Nowadays, almost all the insurance companies have realized the potential of Big Data and started amending their strategies and policies accordingly.
Traditionally, insurance companies had limited data and information to use for risk assessment, fraud claim findings, unique product offerings and to improve customer experiences by offering impeccable services. However, the scenario has changed dramatically after the introduction of Big Data.
First of all, we need to learn what Big Data is. By definition, Big Data is a number of datasets that cannot be assessed by traditional data management tools. These datasets are used to evaluate and access behavioral patterns, consumer preferences and other interactions to offer customized products and services accordingly.
Big Data technology collects data from various sources and evaluates with data management tools to see patterns, trends, and associations to come up with relevant offers for consumers. In the insurance industry, as per the 2015 research, 42% of executives from numerous insurance companies revealed that they use Big Data in offering prices, underwriting, and risk assessment. Also, notably, 77% of them said that they will use in the future to improve the overall business.
There are many ways Big Data can help insurance companies to assess risk, streamline different operations and improve customer experience by offering customized and personalized services. It is a golden opportunity for companies to get a competitive edge over others by implementing Big Data strategy with the help of Big Data Development Companies in their plan.
Here, in this post, we will examine various Big Data use cases which can help the insurance industry to come up with different customer-centric strategies and plans.
Information Gathering:
For example, a user is planning to buy a car insurance plan. Here, Big Data can help the insurance company, to a great extent. You can check out the user's vicinity and evaluate his past driving records for risk assessment. Not just that, Big Data also aids the company to decide the premium and insurance cover amount too. These actionable inputs also help the company to serve the customer conveniently.
With IoT and wearables, companies can get sound information about the insured person’s health and increase the cover and premium amount too.
Here, certain mobile apps too can help the insurance company to access valuable information about users. They can get relevant information such as health insurance, past records of buying healthcare devices and instruments. Such information enables the insurer to come up with the insurance plan that perfectly suits the customer.
Fraud Detection:
Anyone associated with the insurance industry knows that insurance frauds are common. Out of 10 claims, 2 or 3 claims are a fraud. Also, every year, insurance companies make a huge loss due to such false claims. Thanks to the data science and fraud detection techniques, It has become easy to detect fraud claims.
Insurance companies have an algorithm that helps to detect false claims by constantly feeding the data about previous fraudulent activities and illegal patterns in the algorithm. Identifying such fraudulent activities is crucial for companies to lose money, sometimes, huge amounts.
Also Read: How Big Data And Cloud Computing Can Turn Things For The Businesses?
Price Optimization:
Price optimization offers various benefits to the customers and insurance companies too. Coming up with optimized prices for premiums can help to win customer loyalty and also improve revenues and profits.
Here too, different algorithms are used for price optimization. There are various factors considered before reaching the final price such as customer's price sensitivity, previous year's premium charges, expected losses, and costs and predicted risk criteria and previous claims.
All these elements are evaluated and analyzed to optimize the premiums that are most relevant to the current risks associated with the insured person. Though a few insurers believe it a not-so-trustable-practice, the reality is, most of the insurance companies use data for price optimization.
Customer Insight
Prioritizing customers’ preferences is very crucial for any organization to excel. Big Data helps companies to serve this purpose effectively. After the introduction of Big Data technology in the insurance sector, companies can easily make or amend policies and products after evaluating and accessing data from multiple sources.
With such pivotal data in possession, companies can gain vital information about customers’ preferences, demands. They can check out the questions asked by the customers and also help them to find appropriate answers to improve overall customer experience by offering such personalized products and services.
Personalized services:
Offering personalized services is the sure-shot strategy to win the customer’s loyalty. The beauty of this strategy is that it never fails you, irrespective of the business spheres you are in. When you take care of it while proposing the insurance plan to the customer, it will surely give you an upper hand.
With the Big Data technology, insurance companies can make this happen as they have a large chunk of data about customer preferences, interactions, lifestyle patterns, previous buying patterns, interests, hobbies, and demographic preferences. With such data to assist them with, they can easily come up with personalized policies, custom offers, loyalty programs and different payment options as per customers' preferences.
Along with that, consistent communication efforts with the customers about new offerings, latest schemes and premium payment reminders too help to build a personal relationship with customers.
Customer Lifetime Value:
For any organization, evaluating the Customer Lifetime Value (CLV) is momentous. By definition, CLV is a simple calculation of revenues gained from the customer minus expenses made. However, it gives you insight about the critical future relationship scenario with the customer.
If the CLV is higher, you should certainly invest some time to ensure that the customer is happy and satisfied. The CLV prediction is carried out by different behavior models and various factors are taken into consideration to get a clear idea about the possible income in the future. Frequency, monetary value, and recency are some of the factors that help the company to predict future income. With enormous data on your hand, you can easily predict the CLV value by making algorithms at work.
Risk Assessment:
Risk assessment is one of the most important things to determine by insurance companies and Big Data is playing a big role in it. Risk assessment helps companies to decide the policy premiums and insurance coverage amount. There are telematics, IoT devices, and wearables that help companies to assess the risk.
There are various factors taken into consideration such as road conditions, neighborhoods, drivers' past records, and many more. Predictive analysis modeling is used by insurers to predict the risk.
. In the life insurance sector, similarly, with the wearable devices and activity tracking mobile apps, companies can keep track on a user's activities and habits. Even some insurance companies also send personal texts to users about their possible health issues and conditions. Certainly, Big Data has revolutionized the way insurance companies assess risk.
Conclusion:
Big Data has given a paradigm shift to the way companies use data to offer more customized services and ultimately, to improve user experiences. The insurance industry that is completely dependent on data possession and management has gained much with such a large amount of data. Nowadays, almost all the insurance companies have realized the potential of Big Data and started amending their strategies and policies accordingly.
