IndexAbstractIntroductionChallenges and GapsProposed Technology ModelCommunication ModelData ModelSensor No Sensor TypeMajor Use in Insurance SectorAlgorithmsConclusionFuture DirectionAbstractThe current scenario of the health insurance sector in India illustrates that the cost for acquiring medical health has been magnified enormously. Some segments of society remain in the state of distress, while the rest remain healthy. The root cause analysis of the above problem in most cases is not just about inaccessible healthcare services, but a financial system that keeps many segments of society out of the mainstream economy. Recently, the Indian government has been pushing for digital operations, and companies have responded in kind by building interoperable capabilities in technology infrastructures and APIs that connect to unified payment systems. But the bottleneck remains how to include these people who don't have any kind of legal documents or desire to join the mainstream. Say no to plagiarism. Get a tailor-made essay on "Why Violent Video Games Shouldn't Be Banned"? Get an original essay This article proposes a solution to use IOT sensors and mobile technologies to build the financial confidence of the underprivileged sections of the society. Here, 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 rate of adoption of digital services along with health insurance services by people who have no credit history or reliability in terms of material or collateral beneficiaries. All these factors together would lead to inclusive development and help the Indian economy achieve a better position with respect to inclusive development, despite the fact that India has a large informal economy. Introduction The size of the informal economy is huge in India. Therefore, the opportunities in India to expand your business in India are double. The question is how to bring the informal economy into the spotlight. The informal economy not only includes people who do not have accessible banking services, but also people who are otherwise physically or mentally disabled and who have no or little access to medical services. People who live on the fringes of civil society or are marginalized by the current economic order. It's also about people living in their own self-sufficient shell and not needing the intervention of civil society or the mainstream economy. Governments must design, implement and oversee measures to remove obstacles to their growth. It would act as a promoter of inclusive healthcare, banking and social order and help narrow the economic and technological gap between these groups. Technology, innovation and social justice can help us achieve inclusive growth. But for this new growth model it is necessary to adopt fiscal and social orders. These models can be assimilated to the most basic “smart technology” such as mobile phones, which are already part of our daily lives. Mobile technology infrastructure is already widespread across India and the adoption rate is one of the highest even among the poor. The need of the hour is to increase the participation of this population in inclusive growth 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. Consequently, it would arouse interest fromfinancial institutions in supporting capital and resources for inclusive progress. Therefore, the next section describes a real-life scenario that needs attention and technological intrusion for inclusive growth using the method called “Scenario Analysis”. It is a process of analyzing possible future events based on some hypotheses considering possible alternative outcomes (sometimes called "alternative worlds"). Scenario analysis, which is one of the main forms of projection, does not attempt to show a exact image of the future. Instead, it presents several alternatives for future developments. As a result, a set of possible future outcomes can be observed along with the developmental paths that lead to those outcomes. Unlike forecasting, scenario analysis does not rely on extrapolating from the past or extending past trends. It is not based on historical data and does not expect past observations to remain valid in the future. Instead, we try to consider possible developments and turning points, which can only be connected to the past. In short, different scenarios are concretized in a scenario analysis to show possible future outcomes. Each scenario normally combines optimistic, pessimistic, and more and less probable developments. However, all aspects of the scenarios should be plausible. Although widely discussed, experience has shown that approximately three scenarios are most appropriate for further discussion and selection. A larger number of scenarios runs the risk of making the analysis overly complicated. The outcome of such analysis would help us to foresee the possible way in which inclusive growth could occur in the context of inclusive banking, inclusive financial policies, inclusive healthcare, health insurance and inclusive development in general. Challenges and Gaps Current technological disruptions are creating renewed accelerators of the next industrial revolution. The problem, however, still remains how to organize for potential employment and other distributional effects. There is no single ideal policy mix or technological matrix that can be considered for inclusive growth. Limited work has been reported where financial inclusion technology is integrated with health insurance policies and technologies. The level of digitalization does not reflect the real inclusiveness of all sectors of society. Most digital platforms do not accept people without credit history or government compliance like Aadhar card. Open source technologies for inclusiveness of all sectors of society need more attention and encouragement from both the public and private sectors. The full benefit of mobile phone penetration has not been used to fight 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 using such technology for inclusive health insurance. Proposed technological model A new industrial progress is underway. World economies are in an era of rapid transformation that can serve not only elites but all disadvantaged people. s.Communication modelThe first step will consist in the creation of infrastructures for communication and observation of subjects. This concept is similar to the mobile computing paradigm, where useful medical data is accessed from the user's smartphone or a wearable medical sensor. No data is stored on the smartphone nor are changes allowed locally while the sensor data stream is accumulating, but the data is accessible via the cloud storage interface.This is done with the help of integrity protocol like Block Chain. Data Model. This approach requires two levels of data modeling: the first data model belongs to the aspect of financial inclusion and the second will cover the inclusiveness of health insurance. The data relating to the first model will be based on the trajectories of GPRS positions (collected using the mobile operator's services) of subjects registered with the implementing agency (NGO, social entrepreneur or government). The data will consist of location points and the distance traveled by the subject. It will then be sampled at 10 minute intervals. Typically, the dataset will consist of four fields: subject ID, date/time, longitude, latitude. The second layer will be health or medical sensor data, including data on temperature, heart rate, physical distance traveled, and sleep data. For insurance, demographic data such as age, gender, ethnicity and behavioral traits such as smoking, etc. will also be taken into consideration. Example of GPRS data showing the location of a Subject. IoT Sensors: The main key sensor for financial inclusion is mobile-based GPRS sensor data. Besides this, it is assumed that the subject will be registered voluntarily or in an organized manner with some non-governmental agency or the government running this program. The registration process will consist of registering the mobile number with the mobile operator with which the non-governmental body or the government has legal agreements. Apart from this, the person will also have to wear some wearable medical devices mentioned below under which the health insurance policy can be designated with the item. Sensor No Sensor type Primary use in insurance Pedometer (pedometer) or waking distance counter or calorie counter versus exercise sensor. Increased physical activity such as yoga, running, jogging, jumping etc. it reflects on the person's state of health. An active person must be rewarded by the welfare insurance system. This would help measure physical activity and other factors such as muscle mass, etc. Muscle mass and lean 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 the formulation of the wellness insurance package, as a parameter to determine the cost of insurance. Patterns or data sequences that show irregular sleep or rest patterns reflect that the individual is unable to have a normal life due to stress or other factors. Reward systems can also validate sleep patterns with the help of correlation of ECG signal patterns. The subject's health is less likely to go astray in case the subject sleeps on time and gets up properly with a 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, a person is able to maintain a normal range of blood pressure. He or she should be compensated as it poses less risk to the company and the individual. Sugar Analyzers/Glucometer Maintaining the right 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 inif the insured are elderly, frail or pregnant. Adherence to medications 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 health problems. But if a person does not follow medical treatment and does not comply with medications, he does not need to be compensated. Pulse and oxygen sensor This would help find oxygen levels in the blood and help insurance companies find risks associated with conditions such as asthma. Body composition analyzers It is a crucial parameter to determine the risk involved in case the person is overweight. Overweight people are more prone to health problems. So, only the person who maintains the right weight level should be rewarded in the wellness insurance plan. Heart Rate/Beat Analysis This sensor reading provides information about the health of your heart. It is a disease for which insurance coverage is 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 inclusiveness model of health insurance for people in the informal economy. So, the algorithm can be divided into two phases. Qualification of the first step is necessary for the individual to qualify for inclusive health insurance.a) Financial: This step consists of performing a geospatial analysis of the GPRS data and then visualizing the paths taken by the individual. The person himself is considered as a “sensor”, the person's physical location can be captured in three ways to achieve the above-mentioned goal. The first from the mobile sensor receiving GPRS data and the second from the wearable medical device. In both cases it is possible to carry out a frequency analysis to identify the route followed by the subject and the place where he spends most of his time, for example home or temporary sales space. The analysis of the subject's call log and his most frequently called mobile number can also help us verify the stability and solidity of the person's socio-economic context. Therefore, all this data will mainly be a spatial vector model which could be further transformed into a network data model. The study of the subject's mobility data and call log data can be modeled to build the reliability of the subject which determines his qualification to receive benefits from the incumbent agency. Trust Matrix Qualifying = [Call Qualifying Score1, GPRS Qualifying Score3] Trust Matrix Object = [Call Log Data Score, GPRS Log Data Score]; The Trust Matrix can be defined as a matrix that provides 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 the apriori algorithm. It proceeds by identifying frequent individual items in the database and extending them to increasingly larger itemsets as long as those itemsets appear sufficiently often in the database. Apriori uses a "bottom-up" approach, in which frequent subsets are extended one element at a time (a step known as candidate generation), and groups of candidates are tested against the data. The algorithm ends when no further successful extensions are found. This way we are able to find out how the subject frequents at particular points and who is normally on the call with him most of the time. By way of illustration,we can check the following example: Subject 982856489 982856500 982856489 982856500 982856700. A 982856489 982856500 982856600 B 982856489 982856500 982856700 C Illustration for Frequency Analysis of Call Data For this table of call log data, it is clear that most of the calls are initiated by the subject [982856489] and in 50% of the calls are directed to the number 982856500. Thus, 50% of the calls have 98285600 and 982856700 in common. This set is found as a candidate for clustering by the a priori algorithm and shows that the subject communicates more 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 indicate the stability of physical presence [GPRS ​​coordinate data score]. In this way the actual score of the person can be compared with the qualification score matrix to arrive at the decision to include the person in the main financial sector and subsequently for the provision of healthcare through the insurance instrument. b) Health Insurance: Most of the health insurance companies during the risk estimation process take into consideration Two basic parameters, “Age” and “Gender” parameters, along with behavioral traits, will also be taken into consideration. But, in the context of solving our subject inclusiveness problem. Sensor data must also be considered. It should also be noted that not all types of sensor data can be used for this purpose. A simple non-invasive medical sensor would be appropriate for this purpose. The sensor capable of monitoring weight, body temperature, heart rate and walking steps will be useful. The question is how to shape the person's “qualification” for health insurance benefits by the incumbent. The table illustrates how sensor data can be linked for health insurance using the rule of thumb method as an example, if the person qualifies for inclusion for health insurance. Rule of thumb: This rule generally applies to a factor 'X' having a normal or bell-shaped distribution with mean mu “μ” and standard deviation denoted by sigma “σ”. Assuming that the subject's step count time series data behaves normally 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 a sedentary lifestyle can affect 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 agency in charge to provide benefits to the individual. According to the rule of thumb [Ref] the observations (in our case “Physical steps taken on the person's day over time” period) fall within the second and third standard deviation of the mean. Rule of Thumb Deviation Limits[] The average person has a stride length of approximately 2.1 to 2.5 feet. This means that it takes more than 2,000 steps to walk one mile; and 10,000 steps would be almost 5 miles. A sedentary person can only take 1,000 to 3,000 steps per day on average. So, for example, if the values ​​of “physical steps taken in a day” remain in this second or third zone ((μ - σ) and (μ + σ)), there is not much to worry about since the normal capacity of an adult also considering age and sex. But, if the values ​​fall within the first Deviation, it is there.