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Case Tile：
Using Data to Guarantee Health
Case Content：
The “Health and Wellness” consumer category is going through lots of changes． According to a recent Forbes report from The A Group， consumers are more aware of the connection between health， wellness， food， energy， than ever before． For example，“more so than any other generation， B looks to exercise as a way to treat or prevent illness， and it is particularly relevant for emotional and stress-related issues”．
At the same time， the rise of wearable tech， like FitBit and Apple Watch， gives users the ability to collect and monitor their own data． Whether people were competing over how many steps they could take a day， or how many hours they slept， users had an unprecedented level of ease in tracking their every move．
C company， an insurance provider， created a program called “Vitality” to capture value from this change in behavior． Modeled on successful programs from other countries， C realized that they could benefit from the positive and proactive attitude towards preventative health （exercise， diet） and rising levels of comfort with wearable technology that collects data． They created the program“Vitality”， which is novel in that it can monitor the data of consumer's health habits and reward consumers for good behavior． Participants in the Vitality program are provided with a free Fitbit to track their movements． The consumers can win “ vitality points”by making healthy decisions about exercise， which the healthcare provider can then see through the Fitbit data． This can translate into real savings．
“The higher your Vitality Status， the more you can save on your life insurance premiums－up to 10％ a year． You can also earn up to ＄ 600 in annual savings on your healthy food purchases， as well Apple Watch .Series 2 for just ＄25， simply by exercising regularly．”
Instead of a static relationship between healthcare provider and provide， and offline data collection that relies on truthful information， consumers are incentivized to make better decisions about their healthcare and able to improve their deductibles and/or premiums through their healthy choices．
Cis incentivizingthe consumers to make healthier choices benefits the insurance company in several ways． First， it is widely accepted that prevention is better than the cure． The companies are saving themselves money down the road by creating a generation of healthier customers who are motivated to keep themselves healthy． Second， insurance companies can better manage their claims and forecasts with more accurate data and dynamic pricing－they can better service their customers with a better snapshot． Third， insurance companies can start communicating much more data with doctors and physicians， which will not only cut down on administration costs but help both doctor and patient make more informed and accurate choices as opposed to sometimes misleading self-reporting．
C is giving consumers the option to “give out private data for discount in insurance”． For consumers ready to share their data and stay healthy to spend less， C captures a great market．Case Comments：
“The health data trend ＋ insurance companies” is capturing the richness of the data． We can imagine this partnership would be super valuable for basically all health insurance companies． The modern technologies can add value to even such an established business like insurance． Furthermore， this is a perfect economic incentive for people to live healthier lives．
Case Source：
BRODY C． Health insurance remained：John Hancock's vitality［EB/OL］．（2017 －04 －06）［2024－07－01］．https：//d3．harvard．edu/platform－digit/submission/health－insurance－reimagined－john－hancocks－vitality－using－data－to－promote－health．
Case Tile：
Insurance Company Cleverly Utilizes Data Science
Case Content：
Ever since the phrase“ Big Data” was coined in the 1990s， the benefits of leveraging large amounts of data in business have been clear． Yet in 2021， only a quarter of executives described their companies as being data-driven． A insurance company is among the minority that fully embraces data science． How does data science help enhance its predictive capabilities？ The concrete ways are as follows：
Firstly， using data to optimize credit management．
Data science can seem a bit like a crystal ball． But it's actually a combination of machine learning， programming skills and statistical analysis． Using these techniques and tools， A identify patterns in historical data to create models that predict what could happen in the future．
These data-driven models are tailored to meet specific business needs． They can be trained to identify the warning signs of customer dissatisfaction， and on the flip side， find potential new customers． They also play an integral role in risk management， detecting fraud and providing early warning signals of potential financial difficulty among buyers．
Secondly， beyond Excel：predicting bankruptcy risk with machine learning．
While data science is a relatively new field， insurance companies have always worked with actuaries who use similar statistical techniques to predict risks． But what puts data science in a league of its own is that it can comprehensively analyze so much data． Much more than a simple Excel spreadsheet ever could．
Machine learning can gather information from millions of companies， analyzing key factors like debt， liquidity， and country and sector risks． With this information， A is able to highlight which companies may be at risk of non-payment， or even filing for bankruptcy．
However， they also rely on“human power”． They update and adapt the models constantly with the help of risk analysts who review company financials daily and check the model for accuracy． So， while their advice is informed by large volumes of high-quality data， human analysis is the key． Thanks to this powerful combination， A can help their clients make safe， confident decisions and protect themselves against risk．
Thirdly， driving innovation in credit management．
As the insurance industry move further into the digital age， the power of data-driven decision-making is becoming undeniable． At A Trade， they are taking full advantage of this opportunity． They are using data science to improve their predictive capabilities and gain efficiencies by detecting weak signals across a large range of subjects， from fraud detection to debt collection．
But they are not stopping there． As the field of data science evolves， so too do A． A stay at the forefront of the latest technologies and techniques to continuously improve their models，enhance the complement of data science and their expert analysis， and provide clients with the best possible service． The future of data science in trade credit insurance is bright，“we're excited to be leading the way”， the leader of the A trade said that．
Case Comments：
A empowers the company's operational chain with big data technology， utilizing data science and technology to accurately predict credit risk， financial risk， customer churn risk， and other risks in the company's business process． A is a pioneer in the application of big data technology in the insurance industry， which has also given some inspiration to insurance companies in the industry：big data technology can not only be used for specific products， but can also be combined with the company's own construction， providing important protection for internal governance and risk prevention．
Case Source：
TRADE A． How Allianz Trade uses data science to enhance predictive capabilities［EB/OL］．（2023 －03 －21）［2024 －07 －01］． https：//www． allianz － trade． com/en＿ global/news －insights/business－tips － and － trade － advice/How － Allianz － Trade － uses － data － science － to －enhance－predictive－capabilities．html．〉