Big Data for Insurance

By Alex Gayduk, Founder & CEO at Fortifier & Panzly

 

How Big Data is Revolutionizing Business

 
Modern society is continuously producing impressive amounts of real-time data. Processed by artificial intelligence, it 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 enhance customer interactions and make marketing more targeted;
• Process automation – it substitutes manual labor and improves the efficiency of the internal workflow;
• New propositions – it enables creating alternative business models such as peer-to-peer concepts or digital insurers.

 

 

Big Data and Insurance: Implications for Innovation and Competition

 
Insurance was always based on data analysis: accident statistics, policyholder’s personal information, as well as third-party sources help to group people into different risk categories, prevent fraud losses, and optimize expenses. The rapid movement towards the Digital Society opens new sources of information that can be used to create a complex behavioral pattern for each particular 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 – from devices in the Internet of Things such as drones, Smart homes, cars.

Such personal data can complement the traditional sources used in insurance, generating real-time insights about a person's lifestyle and habits that can be used for competitive advantage.

 

The Role of Big Data 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.”

Let’s take a closer look at several Big Data solutions for insurance.

 

Customer Acquisition

 
Every person generates massive amounts of data via social networks, emails, and feedback, which gives much more precise information about their preferences than any questionnaire or survey. Analyzing such unstructured data, insurance companies can increase their efficiency by creating targeted marketing companies that will help to acquire new customers.

 

Customer Retention

 
Based on customer activity, algorithms can identify early signs of customers’ dissatisfaction so you can quickly react and improve your services. Using gathered insights, insurers can focus on solving client’s issues, offer discounts, and even change the pricing model to increase the loyalty of each particular customer.

 

Risk Assessment

 
Insurers were always focused on the verification of customers' information while assessing the risks, and Big Data technologies can increase the efficiency of this process. Before the 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 Prevention and Detection

 
According to Coalition Against Insurance Fraud, each year US insurance companies lose more than $80 billion due to fraud, and this results in increased premiums for every stakeholder. Using predictive modeling, insurers can compare a person's data against past fraudulent profiles and identify cases that require more investigation.

 

Cost Reductions

 
Big Data technology can automate many manual processes, making them more efficient and reducing the costs spent on handling 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 to offer services that will meet the customer's needs. For example, life insurance based on Big Data can become more personalized by taking into account not only the customer’s medical history but also habits detected by activity trackers. It can be also used for determining pricing models that will both ensure profit for companies and fit customers’ budgets.

 

Effects on Internal Processes

 
The implementation of big data algorithms can enhance the efficiency of most processes that require a lot of analyzation. Technology can help insurers quickly check the policyholder’s history, automate claims processing, and deliver better services to customers. According to McKinsley, automation can save 43% of the time for insurance employees, so they can focus on money-generating tasks.

 

How is the Market Evolving by Segments?

 
The insurance industry has already started benefitting from Big Data application; however, the situation is slightly different for each particular sphere.

 

Big Data in Health and Life Insurance

 
Involving new data sources, the industry can develop new insurance models that will not only be more targeted but will also encourage consumers to improve their lifestyle by offering discounts for higher activity. John Hancock has already announced switching 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 that penalizing unhealthy behavior doesn't harm those who really 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 the reported accidents and check their clients' tracks. With Big Data, car insurance can get a highly personalized customer profile based on drivers' GPS locational data and use it to make the final decision. As GPS data is encrypted, such a process doesn't breach clients' 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.

 

What Will the Market Size Be in the Next 3 Years?

 
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. 

 

Predictive Modeling and Big Data Are Insurance Industry Powerhouses

 
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. Using Big Data analytics, insurance can offer personalized policies, precisely assess risks, prevent fraudulent activities, and increase the efficiency of internal processes. 

 
Bio: Alex Gayduk is Founder & CEO at Fortifier & Panzly, Hartford Insurtech Hub mentor. His main focus is on insurance industry digital transformation.

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