The insurance business revolves around measuring and pricing risks. In short, it’s a business about caution. Perhaps that’s the reason why insurance companies have lagged other industries, such as banking, in adopting new technologies that offer more powerful analytics capabilities. But the insurance industry’s reluctance to adopt new technologies is breaking down. So-called insurtech is one of the industries drawing investor interest as startup companies test and market new software solutions, Coin Journal reported, citing a report from CB Insights and KPMG. Insurance companies are interested in using Internet-of-Things technologies to identify and mitigate risk, while also incorporating other technologies that identify fraud, improve efficiency, and cut costs.
Financial services quickly embraced new technology because its use in improving profits was readily apparent to bankers and investors. Gathering information in real-time and timing transactions based on the steady flow of information helps financial industry players make money. In the insurance field, the name of the game is saving money. New technology can help insurance companies more accurately price policies to risk and while also cutting down on fraudulent claims that are costly to the industry. Daily Fintech notes that one of the more transformative developments in insurtech is the emergence of telematics. The capability to gather and transmit near-real time information produces even more data points from which an insurer can more precisely make their risk assessments. These bountiful data sources produce a tremendous amount of data from the home for home insurance, and from the car for property and casualty insurance, Daily Fintech explains.
The collection and analysis of data is not limited to the home and the car. Wearable technology first found its place in people’s lives in fitness applications. But these technologies have matured to a point where they can be used for healthcare applications. The ability to monitor people and collect data of a person over a longer period of time yields measurable data that insurance companies can use to assessing health risks for life and health insurance, Daily Fintech says. Continue reading
Big data and machine learning platforms are in a unique position to analyze one of the most challenging aspects of medical research: behavioral variables that are not reported accurately by common assessment methods. While EMRs (electronic medical records) prompt healthcare providers to collect a great deal of subjective information that impacts healthcare, such as compliance with medication regimes or alcohol intake, the validity of the information collected is questionable.
The subjective nature of the reasons people conceal or alter information given to a health care provider are as complicated at the whole of the human population. People feel social pressures to conform and please a questioner. They don’t want to admit to money problems that impact health care. They do not accurately see their own behavior. Cultural norms regarding personal information vary widely, as does disclosure by age and gender and social class. But new methods of gathering and quantifying data across populations has the potential to give greater insights into human behavior that can change the results of medical research.
Relying solely on patient reports of behavior is a method of gathering data that is extremely limited and may significantly impact the results of healthcare research. But gathering self reports, along with subjective research reports, pharmacy records, laboratory test results, social media, buying behavior, financial records, employment records, and other sources of data, and then analyzing across populations, can give a more accurate picture of what people are actually doing. By having a more accurate picture of human behavioral variables, healthcare research can more accurately assess the impact of human behavior on health care outcomes, and propose treatment modalities that are fine-tuned to the people we actually are. Continue reading
Predictive Analytics is the branch of machine learning that is putting all the data to work. It takes large data sets and uses mathematical algorithms to form predictive models. Then statistical methods such as regression analysis are used to find the variables that influence the models. Finally, machine learning platforms use those predictive models to find patterns in the past that can allow predictions of patterns in the future.
Human behavior can be seen as a series of patterns that repeat, both individually and as a group, over time. This statistical fact doesn’t negate the possibility of free will; it allows us the use of our free will to repeat patterns of behavior that are most comfortable to us, considering the social, cultural, and family pressures that also influence us.
The analysis of patterns of behavior in humans as a group has been the work of historians, who can look back at great sweeps of time and see patterns that repeat. With the amount of data being collected now through online interactions and geotracking tools such as GPS, machine learning platforms are engaged in how to mine that huge amount of data for the very specific data needed to answer questions.
Predicting the future has been an art in which intuition based on expert knowledge and experience was used to make a predictive analysis. Those who are considered masters in their work combine experience with knowledge, and can see patterns from the past and predict patterns into the future. But we are constrained by the depth and breadth of experience and knowledge we can acquire; we are further constrained by unconscious bias and other human attributes. Continue reading
The Social Credit Score is a system that China has had in trials for several years, and that uses the principles of credit scoring- data streams from several specific sources- to formulate a predictive score. The current credit score uses data from the past to predict future behavior, and allows financial institutions to evaluate risk. The social credit score is taking this model and enlarging it to fields of interest beyond financial behavior.
Some data sources are going to provide information that has a better predictive value for future behavior than others. And while humans often surprise their families and themselves by going off the rails, some patterns of behavior are bound to be repeated. The social credit score attempts to find the behaviors with the best predictive value, and use these values to determine how well a person functions in society.
With a large population, concerns of governments are the needs of the population. And population dynamics are different from tribal, family, or individual dynamics. As the world population grows and human society becomes more complex, we will be facing new challenges. We will be living in significantly denser social groups, for instance. Policy decisions will be made for the good of the entire group, and it is believed by those making these plans and decisions that the populations as a whole will be best served if everyone toes the line.
Toe the line. Follow the rules. Do what you are supposed to do. If you screw up, it goes on your permanent record. Very permanent. People can check your score. Employers, landlords, parents of the person you want to marry. Continue reading