Table of ContentsSummaryIntroductionChallenges and GapsProposed Technology ModelCommunication ModelData ModelSensor No Sensor TypePrimary Use in Insurance IndustryAlgorithmsConclusionFuture DirectionSummaryThe Current Scenario of Health Insurance Industry Health insurance in India shows that the cost of acquiring medical health has increased enormously. Certain sections of society remain in a state of affliction, while the rest remain healthy. The root cause analysis of the above problem in most cases is not only unaffordable healthcare services but also a financial system that keeps many segments of society away from the mainstream economy. Recently, the Indian government has been pushing for digital operations and businesses have responded in kind by creating interoperable capabilities in technology infrastructure and APIs that connect to unified payment systems. But the bottleneck remains how to include those people who have no legal documents or who are not inclined to join the mainstream. Say no to plagiarism. Get a tailor-made essay on “Why Violent Video Games Should Not Be Banned”? Get the original essay This article proposes a solution to use IOT sensors and mobile technologies to build financial confidence among the underprivileged sections of society. In this study, data mining methods are used to determine the inclusiveness factor score from mobile and health sensor data. The expected result is an increase in the adoption rate of digital services as well as health insurance services by people who do not have a credit history or reliability in terms of material beneficiaries or collateral. Together, all these factors would lead to inclusive development and help the Indian economy position itself in a better position for inclusive development, despite the fact that India has a large informal economy.IntroductionThe size of the economy informality is huge in India. Therefore, the opportunities in India are twofold to develop your business in India. The question is how to integrate the informal economy into the main group. The informal economy includes not only people who do not have accessible banking services, but also people who suffer from different physical or mental disabilities and who have little or no access to medical services. People who live on the margins of civil society or who are marginalized by the current economic order. These are also people who live in their own shells and do not need intervention from civil society or the economy. Governments must design, implement and supervise measures to remove obstacles to their growth. It would act as a promoter of inclusive health, banking and social order and help reduce economic and technological gaps between these groups. Technology, innovation and social justice can help us achieve inclusive growth. But for this new growth model, fiscal and social orders must be adopted. These models can be compared to the most basic iotized “smart technologies” such as cell phones which are already part of our daily lives. Mobile technology infrastructure is already widespread across India and the adoption rate is also one of the highest among the poor. Now is the time to increase the participation of this population in growthinclusive and increase innovation in the development of inclusive technologies. This would have a direct or indirect impact on the use of services used by major economies, such as health insurance. Therefore, this would spark the interest of financial institutions to support capital and resources towards inclusive progress. Therefore, the following section describes a real-life scenario that requires special attention and technological intrusion for inclusive growth using the method called “Scenario Analysis”. It is a process of analyzing possible future events based on a hypothesis by considering alternative possible outcomes (sometimes called "alternative worlds). Thus, scenario analysis, which constitutes one of the main forms of projection, does not attempt to give an exact picture of the future. Instead, it presents several alternatives for future developments. Therefore, a range of possible future outcomes can be observed along with the developmental pathways leading to these outcomes. Unlike predictions, scenario analysis does not rely on extrapolating from the past or extending past trends. It does not rely on historical data and does not expect past observations to remain valid in the future. Instead, it attempts to envisage possible developments and turning points, which can only be linked to the past. In short, multiple scenarios are fleshed out in a scenario analysis to show possible future outcomes. Each scenario normally combines optimistic, pessimistic and more or less probable developments. However, all aspects of the scenarios must be plausible. Although much debated, experience has shown that three scenarios are most appropriate for further discussion and selection. A greater number of scenarios carries the risk of making the analysis too complicated. The outcome of such an analysis would help us predict how inclusive growth could occur in the context of inclusive banking, inclusive financial policies, inclusive healthcare, health insurance and development inclusive global. Challenges and Gaps Current technological disruptions are creating new drivers for accelerating the next industrial revolution. However, problems remain: how to organize for potential employment and other distributional effects. There is no single policy mix or ideal technology matrix that can be considered for inclusive growth. Limited work has been reported where financial inclusion technology is integrated into health insurance policies and technologies. The level of digitalization does not reflect the true inclusiveness of all sections of society. Most digital platforms do not accept people without credit history or government compliance, like Aadhar card. Open source technologies for the inclusion of all sections of society require more attention and encouragement from the public and private sectors. Mobile phone penetration was not taken to combat poverty or increase financial inclusion. There are few references where this technology has been used to help people in financial difficulty, but no references to the use of such technology for inclusive health insurance. Proposed technological modelA new industrial progress is underway. Global economies are in an era of rapid transformation that can serve not only the elites but also all disadvantaged people. s.Communication modelThe first step will be to set up a communication infrastructurecommunication and observing subjects. This concept is similar to the mobile computing paradigm, in which useful medical data is accessible from the user's smartphone or wearable medical sensor. No data is stored on the smartphone and no changes are allowed locally during the accumulation of the sensor data stream, but the data is accessible via the cloud storage interface. This is done using the integrity protocol such as Block Chain. Data Model This approach requires two levels of data modeling: the first data model belongs to the financial inclusion aspect and the second will be aimed at the inclusiveness of health insurance. Data relating to the first model will be based on GPRS location trajectories (collected using mobile operator services) of subjects registered with the implementing agency (NGO, social entrepreneur or government). The data will consist of the location points and the distance traveled by the subject. Then it will be sampled later at an interval of 10 minutes. Typically, the dataset will consist of four fields: subject ID, date/time, longitude, latitude. The second level of data from health or medical sensors will include temperature data, heart rate, physical walking distance, and sleep data. For insurance, demographic data such as age, gender, ethnicity, and behavioral traits such as smoking, etc. are collected. will also be taken into account. Examples of GPRS data showing the location of a subject. IoT sensors: The main key sensor for financial inclusion is data from mobile GPRS sensors. Other than that, it is assumed that the subject will be voluntarily or in an organized manner registered with a non-governmental or government agency that operates this program. The registration process will involve registering the mobile phone number with the mobile operator with whom the non-governmental agency or government has a legal agreement. Apart from this, the person will also have to carry certain portable medical devices given below based on which the healthcare policy can be designated with the subject. Sensor No sensor type Main use in insurance industry Step counter (pedometer) or wake-up distance counter or calorie counter versus exercise sensor. Increased physical activity like yoga, running, jogging, jumping, etc. is reflected in the state of health of the person. An active person should be rewarded by the welfare insurance system. This would help measure physical activity and other factors like muscle mass etc. Muscle mass and lean body mass calculations become important when a person has opted for a supervised weight loss program initiated by the insurer. Sleep/Rest Pattern Analyzers Its main use is in formulating the wellness insurance package, as a parameter to determine the cost of insurance. Patterns or sequences of data showing irregular sleep or rest patterns reflect that the subject is unable to lead a normal life due to stress or other factors. Reward systems can also validate sleep patterns using correlation of ECG signal patterns. The health of the subject is less likely to go astray in case the subject sleeps on time and wakes up properly with deep and complete sleep.3 Blood Pressure Analyzers Blood pressure is a vital statistic of the body to estimate the state of health of the subject. Today, in this stressful life, if a person is able to maintaina normal range of blood pressure. He or she should be rewarded because it poses less risk to the company and the individual. Sugar analyzers/glucometer Maintain the correct range of HDL (high density lipoprotein), LDL (low density lipoprotein), TL (total cholesterol) and triglycerides and fats. Acids are essential for a person to stay healthy. Therefore, insurance companies use this data to calculate risk. Body temperature sensor Temperature becomes an important factor for observing and calculating risks, especially in cases where policyholders are elderly, frail or pregnant. Medication Adherence The logic of wellness insurance is based on the assumption that the person does not want to get sick and if he is sick, he will try to get out of his health problems. But if a person does not follow medical treatment and does not comply with medications, he does not need to be rewarded. Pulse and Oxygen Sensor This would help determine the oxygen levels in our blood and help insurance companies detect associated risks. with conditions like asthma. Body composition analyzers This is a crucial parameter to determine the risk incurred in the event of overweight. Overweight people are more prone to health problems. Therefore, only the person who maintains a good weight level should be rewarded with a wellness insurance plan. Heart Rate and Beat Analysis This sensor reading provides information about heart health. It is a disease for which insurance coverage is also sometimes denied. Using Sensor data, insurance companies can identify the degree of associated risk and calculate the premium accordingly. Algorithms The objective of this research work is to model financial inclusion and build the health insurance inclusiveness model for people in the informal economy. Hence the algorithm can be divided into two steps. Qualification is the first step necessary for the subject to be qualified for health insurance inclusiveness. a) Financial: This step consists of carrying out a geospatial analysis of the GPRS data and then visualizing the routes taken by the subject. The person himself is considered a “sensor”. The physical location of the person can be captured in three ways to achieve the aforementioned goal. First from the mobile sensor which collects GPRS data and then from the portable medical device. In both cases, a frequency analysis can be carried out to identify the route taken by the subject and the place where they spend the most time, such as the house or a temporary retail space. Analyzing the subject's call log as well as the most frequently called mobile number can also help us determine the stability and strength of the person's socio-economic circle. Therefore, all this data will primarily be a spatial vector model which can then be transformed into a network data model. Studying the subject's mobility data and call log data can be modeled to enhance the subject's reliability in determining their qualification to receive benefits from the historical agency. Trust Matrix Qualifying = [Call Qualifying Score1, GPRS Qualifying Score3] Trust Matrix Subject = [Call Qualifying Score, GPRS Log Data Score]; The trust matrix can be defined as a matrix that gives three types of scores, calculated based on frequency analysis of call log data and geospatial data. Frequency analysis of all these datasets is performed using an a priori algorithm. It proceeds by identifying the elementsfrequent individual items in the database and expanding them to larger and larger item sets, provided that those item sets appear frequently enough in the database. Apriori uses a bottom-up approach, in which frequent subsets are expanded one item at a time (a step known as candidate generation) and groups of candidates are tested against the data. The algorithm terminates when no other successful extensions are found. Using this method we can determine how the subject frequents particular locations and who is normally with them on calls most of the time. For illustration, we can check the example below: Subject 982856489 982856500 98285600 982856489 982856500 982856700. A 982856489 982856500 982856600 B 982856489 98 2856500 982856700 C Illustration for frequency analysis of call data For this data table From the call log, it is clear that most calls are initiated by the subject [982856489], and that 50% of calls are directed to the number 982856500. Then, 50% of calls have 98285600 and 982856700 in common. This set is found as a candidate for clustering by the a priori algorithm and this shows that the subject communicates most frequently with these people. And if this process continues for more than about six months. It can be deduced that the subject has a stable socio-economic relationship [social network score] with people and the GPRS coordinates will provide information on the stability of the physical presence [GPRS ​​coordinate data score]. This way, the person's actual score can be compared to that of the qualifications. score matrix to arrive at a decision to include the person in the main financial sector and later for health care to be given to him through the insurance instrument. b) Health Insurance: Most health insurance companies, during their risk estimation process, take into account two basic measurements, the parameters "Age" and "Gender", as well as the traits of behavior will also be taken into account. But, in the context of solving our problem of subject inclusiveness. Sensor data must also be taken into account. It should also be noted that not all types of sensor data can be used for this purpose. A simple non-intrusive medical sensor would be suitable for this purpose. The sensor that can check weight, body temperature, heart rate and walking steps will be useful. The question is how to model the individual's "Qualification" for the holder's health insurance benefits. The table shows how sensor data can be linked for health insurance using the rules of thumb method for illustration, if the person qualifies for inclusive health insurance. Rule of thumb: This rule generally applies to a factor “X” having a normal or bell-shaped distribution with a mean mu “µ” and a standard deviation denoted sigma “σ”. Assuming that the time series data on the subject's number of walking steps normally behaves like a normal distribution. Typically, an adult's ability to walk a certain distance per day remains within a particularly narrow normal range, but eating disorders and other factors such as sedentary lifestyle can impact this. healthy habit. He or she may become weak or underweight over time due to certain malnutrition problems. Therefore, there is a need for a point or credit system that can help the existing agency provide benefits to the subject. According to the rule of thumb..