Editor’s note: The Intersection blog focuses primarily on the property & casualty insurance business in Canada. However, we occasionally take stock of interesting insights relating to other parts of the insurance community and other parts of the world, as we did here.
Big Data has become a big buzzword, particularly in the insurance industry. It holds the unique ability of utilizing predictive modelling to work out future trends by looking at information from past data. The ability to predict future trends has attracted the attention of many insurance companies, one important reason for this being that analytics can help us better predict and understand risk and customer behaviour. This article reveals how big data has influenced the insurance industry by looking at the latest trends in international private medical insurance inflation.
The case of international private medical insurance inflation
The average costs of international private medical insurance (IPMI) in most countries are rising every year. Insurance advisor Pacific Prime’s Cost of Health Insurance Report found that in Canada, the average cost of IPMI in 2016 is USD 10,870 across all demographics, a striking increase from 2015 when the average cost was USD 9,100. There are many reasons behind inflating premiums, such as the increased demand for quality private international care, as well as the increased cost of healthcare worldwide. Interestingly, Pacific Prime’s upcoming report on IPMI trends revealed that the IPMI inflation rate in 2016 was 9.2%, the exact same percentage as the inflation rate in 2015, despite the company’s previous predictions of a slightly higher inflation rate. The report further found that IPMI inflation rates have been slowing down over the past couple of years, and big data is likely to have played a bit part in this trend.
How are insurers leveraging big data?
According to one of Pacific Prime’s partners, Bupa Global, they are “seeing companies request data for many different aspects, including everything from service standards to quality outcomes to health care costs to even claims habits”. So, how are insurers leveraging big data? Arguably, one of its most important uses is to more efficiently set policy premiums by better understanding and predicting risks from analytics. This helps combat the problem of low investment returns from unprofitable underwriting. One of the reasons contributing to unprofitable underwriting is health care fraud, and, as such, one of the main methods that insurers are using to combat this is by raising premiums. With the help of big data, insurers can better identify fraudulent claims by matching variables within each claim with past claims known to be fraudulent. Data analytics is becoming increasingly sophisticated, and certain behaviours specific to fraudulent individuals that may have bypassed manual human assessment are becoming increasingly detectable through big data’s ability to examine mass amounts of data, thus increasing the effectiveness of accurate claims assessment.
The future implications of big data
There already exists many new revolutionary ways that insurers are utilizing big data in recent years. For example, some insurers are even offering insurance services based on data collected from wearable fitness trackers. We predict that with further advancements in technology, insurance companies will benefit from the ever-improving integration of big data that will further optimize setting premiums, as well as help gather relevant insights to reach new customers and develop new products. Moving forward, another future implication that may affect insurance decision makers will likely involve issues around combating privacy concerns from big data technology.
About the authors
Andrew Ma is the Managing Director for Pacific Prime, an expatriate health insurance broker that provides professional advice on various health insurance solutions to expats who are living and working abroad.
Kylie Taylor, the executive editor for Pacific Prime, was co-author on this post.
