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Big data is one of the emerging components of the insurtech sphere that is not widely used yet but is highly demanded. On the whole, insurance is a commercial sector that can truly benefit from Big data insight, perhaps more than any other industry. The appropriate application of big data enables start-ups to disrupt a traditional widespread insurance policy model (that has not been changed for many years). Most of the contemporary insurers really understand and even believe that innovation should be introduced into their activity rapidly in order to provide a higher level of customer service and also to be a competitive player in the market.
Insurtech businesses try to use data to change the long-established traditional insurance model. They do their utmost to offer adaptable and agile policies that are in line with our modern-day behavior patterns. The use of such technology gives insurers strategic advantages over the competition. So, it’s not surprising that the size of their share in today’s insurance industry is on the rise.
5 Ways Big Data Analytics Influences the Insurance Sector
It is well-known that the insurance industry deals with a wide pool of valuable consumer data:
- Open data
- Social media data
- Data from Internet devices (IoT)
With the use of Big Data tech, all of this information can be collected and analyzed by artificial intelligence and machine learning. It allows us to get a detailed and most accurate picture of organizations and individuals applying for certain insurance.
Such a revolutionary approach lets Big Data Analytics transform:
1. Pricing and Underwriting
The insurance sphere has used the ability to assess accurately possible risks for many years. But today insurers, who get vital access to big data sets, can fulfill an assessment at a level of detail and speed that was previously unfathomable. It gives the possibility to reveal more potential risks to a customer. But at the same time, we can’t say that Big Data has replaced traditional data sources (demographic or exposure data) totally. Today they are combined to create a new approach in the insurance sphere.
2. Wellbeing and Health
IoT-powered modern wearable tech has the potential to bring a great impact on each person’s individual health insurance sphere. The individual data gathered by such devices as the well-known Fitbit or useful Apple Watch’s health application gives insurers a wonderful opportunity to customize insurance plans to the behavior and health individually in each particular case.
In general, health insurance companies get detailed vital information from different big data sources due to the following vital aim – to recommend both preventative and immediate care to their customers. For example, Oscar, a health insurance company that is based in New York, has been fulfilling it for several years now by means of connecting with several electronic medical records (EMRs) in various states in order to use a patient’s medical history. They do this to create permission-based predictions and also valuable recommendations to their consumers on their modern platform.
3. Claims and Settlements
It is known that the claims process is usually very long. The claim must be assessed and the liability must be correctly determined to confirm a payout’s justification. But Big Data and AI help not only to segment but also to analyze the information much faster while reducing the possibility of human error. Many companies aim at the claims process full automation.
4. Tailored Insurance and Policy Developing
It means the process of further deep personalization of the insurance sphere. By getting more detailed information and its further segmenting, insurers can offer policies and strategies for each specific situation and to a wider range of clients.
For instance, Timothy Partasevitch, the Chief Growth Officer at Smart IT, had a chance to interview Bryan Falchuk, Founder and Managing Partner at Insurance Evolution Partners. Bryan had this to say, when asked, “What is the greatest challenge for digital transformation in the Insurance sector, going forward?”
“It is not a function of technology or resources today. The real constraint is willingness. There has been an explosion of new providers and tools to solve so many of the needs insurers or their customers have. And enough players in the industry have moved to fully-Cloud-based platforms that we know it is possible. When I see difficulty, it always boils down to the willingness of the leadership to make a big change. If we’ve seen anything over the past 18-or-so months it’s that the train has left the station in terms of what customers expect of us, what they aren’t willing to tolerate in terms of friction in the customer experience, and how many competitors are showing that the industry can innovate and change.”
Bryan Falchuk, Founder & Managing Partner, Insurance Evolution Partners
5. Traditional Features of Customer Experience
We can observe a new wave of widespread use of AI, as well as ML-powered Big Data analytics. It helps to offer more personalized effective customer service experiences. Today computers are expected to handle important tasks and roles of decision-making more often.
More detailed profiles of customers can be created with the help of ML and AI. They can tap into external datasets seeking different similarities in internal data. With the help of understanding these datasets waiting times can be reduced.
This can be achieved in call centers by connecting a customer with a chatbot first, which will collect and sort the info concerning call volume, reasons, and existing types of customers who call at a particular time of the day.
And then customers will be connected with a customer service representative with due qualification at the most appropriate time. It is not surprising that many companies have already decided to replace human representatives with data-driven AI and also ML-powered modern chatbots.
So, let’s summarize and add more details to obvious trends occurring with Big Data for the Insurance sector:
Machine Learning
Machine learning capabilities, as well as various insurance data analytics solutions, develop rapidly. They help to process all the necessary information with high efficiency and accuracy. As a result, they can help to improve pricing strategies and claims processing.
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Predictive Analytics
Predictive analytics efficiently helps the insurance industry to forecast all possible risks and rewards. It uses Big Data which is collected by insurers to make accurate precise calculations concerning claims triage, emerging trends, risk, and pricing selection. Using predictive analytics valuable capabilities, competent insurers can speed up lots of operations and important processes.
Data Privacy and Protection
Insurance companies pay due attention to new laws and regulations concerning how they, as well as their analytics teams, can operate. It’s important for insurers to use a data system that is scalable and flexible. In this case, they will stay in compliance as regulations and laws continue to change with time.
Internet of Things (IoT)
IoT helps insurers to get access and insights, unlike anything they have had before. At the same time, it needs more data security and thoughtful regulation.
The IoT plays a great role in Big Data analytics in the Insurance sphere. Insurers get the opportunity to gather and sort detailed information. As a result, IoT insurance data can help to improve the existing claims processing and leakage, risk assessment, product pricing, etc.
Unstructured Data
Unstructured data becomes a new keystone for modern insurance data analytics. It is presented by:
- Social media data
- Various multimedia
- Written reports of different types
It has become possible to get such information due to new technologies. The IoT, for example, uses a method of unstructured data analysis and mining to create a robust customer and consumer profile.
Artificial Intelligence (AI)
Artificial Intelligence disrupts the Insurance sphere in different directions – processing, underwriting, and customer service. AI can also be used as a central power hub that drives many powerful automated tools, including powerful machine learning and useful predictive analytics. It allows to increase speed, optimize different processes and generate emerging insights, thus improving insurance big data analytics.
Data Availability
Data availability level increases. Today more and more insurers have systems to ensure high levels of modern data availability. So, they can access any necessary data their business needs for functioning, even in the case when any disruption occurs.
Blockchain Data
Blockchain data has the potential to transform the insurance industry. Its potential is really multileveled. At the basic level, it enables more secure data exchange between two sides – insurers and customers. At the same time, blockchain in big data insurance analytics can also be used to build applications concerning competent risk assessment to speed up claims processing and to improve the legitimacy and accuracy of data according to legal standards.
Telematics
Telematics is an important trend of data collection in the sphere of Insurance. It uses well-known sensor technologies for collecting and further qualitative transmitting of gathered real-time data even over rather long distances. Its ability to change customer behavior is really vital. For example, consumers drive more safely, when their movements are tracked. And insurance companies can save on claims processes in this case.
So, it is obvious to everyone that the global digital transformation takes place and develops rapidly in the insurance industry. And with the emergence of new players using vital Big data analysis, as well as Predictive analytics, insurers must adapt and develop quickly not to lose positions in the modern market.