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 modern digital era, Big Data technologies help to process vast amounts of information, increase workflow efficiency, and reduce operational costs. Learn more about the benefits of Big Data for insurance from our material.
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-advisors, and chatbots enhance customer interactions and make marketing more targeted;
- Process automation – it substitutes manual labour 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 behavioural pattern for each particular customer and precisely determine his or her risk class.
There are two new data sources:
- Online behaviour – this includes social media activity, online shopping behaviour, 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. Therefore generating real-time insights about a person’s lifestyle and habits that can be used for competitive advantage.
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.”
Big Data solutions for insurance.
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. Analysing such unstructured data, insurance companies can increase their efficiency by creating targeted marketing companies that will help to acquire new customers.
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 change the pricing model to increase customer loyalty.
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 modelling 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 USD$80 billion due to fraud, and this results in increased premiums for every stakeholder. Using predictive modelling, insurers can 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 on handling claims and administration. In a competitive environment, this will result in lower premiums, which will attract new clients.
Personalised 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 personalised by taking into account not only the customer’s medical history but also habits detected by activity trackers. It can also be 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 analysation. 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 that 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
By Involving new data sources, the industry can develop new insurance models. This will not just be more targeted but also encourage consumers to improve their lifestyle.
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 penalising unhealthy behaviour doesn’t harm those who need protection.
Big Data in property and casualty insurance
The situation is more promising for property and casualty insurance, as Big Data can help to detect empirical links between customer behaviour 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 personalised 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 reasonably 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 continually increasing, and insurance companies are expected to invest in these technologies up to USD$3.6 billion by 2021, according to SNS Telecom and IT.
Big Data implementation results in 30 per cent better access to insurance services, 40-70 per cent cost savings, and 60 per cent 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 behaviour and gather real-time insights for both established insurance enterprises and InsurTech startups. Using Big Data analytics, insurance can offer personalised policies, assess risks, prevent fraudulent activities, and increase the efficiency of internal processes.