How Big Data Impacts The Insurance Industry And Beyond
Big data is revolutionizing business. Here’s an inside look at the tangible benefits of big data when applied to the insurance industry.
The insurance industry has always been quite conservative. However, the adoption of new technologies is not just a modern trend but a necessity to maintain a competitive pace. In the digital era, big data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs.
Here’s an inside look at the tangible benefits of big data when applied to the insurance industry.
Big data is revolutionizing business
In today’s society, we are continuously producing impressive amounts of real-time data.
For perspective, “By the time you finish reading this sentence, there will have been 219,000 new Facebook posts, 22,800 new tweets, 7,000 apps downloaded, and about $9,000 worth of items sold on Amazon… depending on your reading speed, of course. Now that the Internet is widely available, just one second of global online activity is jam-packed full of events, from communication with others to data storage to entertainment options galore.”
Processed by artificial intelligence (AI), data becomes a valuable source of information vital for most business models, including insurance.
Big data is mainly used for:
* New distribution models – virtual assistants, robo-advisers, and chatbots to enhance customer interactions and make marketing more targeted;
* Process automation – substituting manual labor and improving the efficiency of the internal workflow;
* New propositions – enabling the creation of alternative business models such as peer-to-peer concepts or digital insurers.
Big data and insurance: Implications for innovation and competition
The insurance industry has always been driven by data analysis: accident statistics, a policyholder’s personal information, as well as third-party sources that help to group people into different risk categories, prevent fraud losses, and optimize expenses. The rapid movement towards a digital society presents new sources of information that can be used to create a complex behavioral pattern for each customer and precisely determine his or her risk class.
There are two new data sources:
* Online behavior – this includes social media activity, online shopping behavior, browsing activity, etc.
* Sensor data – data from devices in the Internet of Things (IoT) such as drones, smart homes, cars, etc.
Such personal data can complement the traditional sources used in insurance, and your industry, to generate real-time insights about a person’s lifestyle and habits that can be used to create a competitive advantage.
Big data’s role in the insurance industry
As Anna Maria D’Hulster, Secretary General at The Geneva Association, suggests,“Going forward, access to data and the ability to derive new risk-related insights from it will be a key factor for competitiveness in the insurance industry. New approaches to encourage prudent behavior can be envisaged through Big Data, thus new technologies allow the role of insurance to evolve from pure risk protection towards risk prediction and prevention.”
- Photo: © Halfpoint, YFS Magazine
Let’s take a closer look at several big data solutions for insurance.
Every individual generates massive amounts of data via social networks, emails, and feedback, which provides much more precise information about their preferences than any questionnaire or survey ever could. Analyzing such unstructured data, insurance companies can increase their efficiency by creating targeted marketing capacities that will help acquire new customers.
Based on customer activity, algorithms can identify early signs of customer dissatisfaction so you can quickly respond and improve your products and services. Using collected insights, insurers can focus on solving the client’s issues, offer discounts, and even adjust the pricing model to increase loyalty on an individual basis.
Insurers were always focused on the verification of customer information while assessing the risks. Big data technologies can increase the efficiency of this process. Before reaching a final decision, an insurance company can use predictive modeling to estimate possible issues based on the client’s data and precisely determine their risk class.
Fraud detection and prevention
According to the Coalition Against Insurance Fraud, each year, US insurance companies lose more than $80 billion due to fraud, resulting in increased premiums. The use of predictive modeling allows insurers to compare a person’s data against past fraudulent profiles and identify cases that require more investigation.
Big data technology can automate many manual processes, making them more efficient and reducing the costs spent to handle claims and administration. In a competitive environment, this will result in lower premiums, which will attract new clients.
Personalized services and pricing
The analysis of unstructured data can help companies offer services that will better meet their customer’s needs. For example, big data-based life insurance can be personalized with consideration for a customer’s medical history and habits that are detected by activity trackers. It can also be used to determine pricing models that ensure a profit and meet client budgets.
Internal process optimization
The implementation of big data algorithms can enhance the efficiency of most processes that require a lot of analysis. Technology can help insurers quickly check a policyholder’s history, automate claims processing, and deliver more reliable services to customers. According to McKinsey, automation can save 43% of the time for insurance employees, so they can focus on money-generating tasks.
How the insurance market is evolving, by segment
The insurance industry has already started to benefit from the application of big data. However, the situation is slightly different for each particular segment.
Big data in health and life insurance
Involving new data sources, the industry can develop new insurance models that are targeted and encourage consumers to improve their lifestyle by offering discounts for higher activity. John Hancock has already announced their switch to interactive policies based on data generated by fitness trackers and health apps.
But the implication of big data in health insurance causes concerns related to data security, privacy, and ethics. This field still requires legislation to ensure penalizing unhealthy behavior doesn’t harm those who need protection.
Big data in P&C insurance
The situation is more promising for property and casualty insurance, as big data can help to detect empirical links between customer behavior and risks. For example, car insurance companies can grade roads based on reported accidents and check their clients’ tracks. With big data, car insurers can develop a highly personalized customer profile based on drivers’ GPS locational data and use it to make decisions. As GPS data is encrypted, such a process doesn’t breach client privacy.
Big data in travel insurance
Compared to other segments, travel insurance adopts big data and AI technologies particularly well. The relatively low policy price makes travel insurance a fairly quick decision, so this industry deals with an impressive number of requests. Technologies can speed up the interaction with customers, give more tailored products and services, automate simple communication, improve customer satisfaction, and quickly configure the most beneficial offer.
The future of big data and insurance
The adoption of big data is constantly increasing, and insurance companies are expected to invest in these technologies up to $3.6 billion by 2021, according to SNS Telecom & IT. Big data implementation results in 30% better access to insurance services, 40-70% cost savings, and 60% higher fraud detection rates, which is beneficial for both insurers and stakeholders. The combination of big data and insurance will facilitate the adoption of on-demand models and new underinsured risks, for example, cybercrime.
The continuous analysis of consumer data makes it possible to understand customer behavior and gather real-time insights for both established insurance enterprises and InsurTech startups. By using big data analytics, insurance companies can offer personalized policies, precisely assess risks, prevent fraudulent activities, and increase the efficiency of internal processes.
Fuente: Alex Gayduk
Founder and CEO at Fortifier & Panzly, and a Hartford Insurtech Hub mentor. His main focus is on the insurance industry’s digital transformation.
Can You Trust Your Data?
The Data Trust Gap
In The State of Data Management — The Impact of Data Distrust, a rec
The New Change Management: Automated And Decentralized
This is the topic of my recently updated research, “Overcome Change Management Paralysis.” In this report, I cover the reasons for th
Decentralization Becomes a Question of Demand
Kevin Ji, the senior director analyst at Gartner, agreed. He observed that centralization and decentralization are driven by demand for c