Model of decision support system used for assessment of insurance risk

Za savremeno poslovanje u uslovima neizvesnosti, rezultati predviđanja poslovanja su od suštinskog značaja za evaluaciju buduće finansijske efikasnosti preduzeća. U radu je izložen primer predviđanja premija na osnovu ocena izvora rizika u osiguranju. Zbog neizvesnosti koja prati trenutak nastanka i iznosa štete neophodno je osigurati dovoljno sredstava za pokriće rizika. Za usklađivanje sredstava i obaveza potrebno je oceniti uticaj rizika na promenu premije po vrstama osiguranja, što čini osnovni koncept razvoja i poslovanja osiguravajućih društava. U radu je predstavljeno eksperimentalno istraživanje rangiranja rizika na osnovu projektovanog modela u sistemu za podršku odlučivanju. Sistem za podršku odlučivanju korišćen je u cilju generisanja hijerarhije uticajnih kriterijuma i alternativa u modelu za ocenu rizika kod navedenih vrsta osiguranja. Predloženi model zagovara ideju da se za vrste osiguranja kod koje se utvrdi najviši stepen rizika i na osnovu toga donesu odluke o visini premije u narednom periodu. .In order to run a modern business in uncertain times, business forcasting is very important for evaluation of company.s future financial performance. This paper shows an example of premium forecast based on the assessment of risk sources in insurance system. Due to uncertainty that is one of the characteristics of loss occurrence and indemnity amount, it is important to hold sufficient assets to cover the risk. For asset-liability matching, one should first assess the impact of risk on premium movement per insurance lines. This is the main concept of development and performance of insurance companies. This paper shows an experimental research of risk ranking based on projected model of decision support system. Decision support system is used with the aim to generate hierarchy of influential criteria and alternatives of risk assessment model for stated insurance lines. Suggested model sup- ports the idea according to which one should first determine insurance lines with the highest risk and then, on that basis, make a decision on premium amount in the following period

Model of decision support system used for assessment of insurance risk

Za savremeno poslovanje u uslovima neizvesnosti, rezultati predviđanja poslovanja su od suštinskog značaja za evaluaciju buduće finansijske efikasnosti preduzeća. U radu je izložen primer predviđanja premija na osnovu ocena izvora rizika u osiguranju. Zbog neizvesnosti koja prati trenutak nastanka i iznosa štete neophodno je osigurati dovoljno sredstava za pokriće rizika. Za usklađivanje sredstava i obaveza potrebno je oceniti uticaj rizika na promenu premije po vrstama osiguranja, što čini osnovni koncept razvoja i poslovanja osiguravajućih društava. U radu je predstavljeno eksperimentalno istraživanje rangiranja rizika na osnovu projektovanog modela u sistemu za podršku odlučivanju. Sistem za podršku odlučivanju korišćen je u cilju generisanja hijerarhije uticajnih kriterijuma i alternativa u modelu za ocenu rizika kod navedenih vrsta osiguranja. Predloženi model zagovara ideju da se za vrste osiguranja kod koje se utvrdi najviši stepen rizika i na osnovu toga donesu odluke o visini premije u narednom periodu. .In order to run a modern business in uncertain times, business forcasting is very important for evaluation of company.s future financial performance. This paper shows an example of premium forecast based on the assessment of risk sources in insurance system. Due to uncertainty that is one of the characteristics of loss occurrence and indemnity amount, it is important to hold sufficient assets to cover the risk. For asset-liability matching, one should first assess the impact of risk on premium movement per insurance lines. This is the main concept of development and performance of insurance companies. This paper shows an experimental research of risk ranking based on projected model of decision support system. Decision support system is used with the aim to generate hierarchy of influential criteria and alternatives of risk assessment model for stated insurance lines. Suggested model sup- ports the idea according to which one should first determine insurance lines with the highest risk and then, on that basis, make a decision on premium amount in the following period

HEALTH CLAIM INSURANCE PREDICTION USING SUPPORT VECTOR MACHINE WITH PARTICLE SWARM OPTIMIZATION

The number of claims plays an important role the profit achievement of health insurance companies. Prediction of the number of claims could give the significant implications in the profit margins generated by the health insurance company. Therefore, the prediction of claim submission by insurance users in that year needs to be done by insurance companies. Machine learning methods promise the great solution for claim prediction of the health insurance users.  There are several machine learning methods that can be used for claim prediction, such as the Naïve Bayes method, Decision Tree (DT), Artificial Neural Networks (ANN) and Support Vector Machine (SVM). The previous studies show that the SVM has some advantages over the other methods. However, the performance of the SVM is determined by some parameters. Parameter selection of SVM is normally done by trial and error so that the performance is less than optimal. Some optimization algorithms based heuristic optimization can be used to determine the best parameter values of SVM, for example Particle Swarm Optimization (PSO) and Genetic Algorithm (GA). They are able to search the global optimum, easy to be implemented. The derivatives aren’t needed in its computation. Several researches show that PSO give the better solutions if it is compared with GA. All particles in the PSO are able to find the solution near global optimal. For these reasons, this article proposes the health claim insurance prediction using SVM with PSO. The experimental results show that the SVM with PSO gives the great performance in the health claim insurance prediction and it has been proven that the SVM with PSO give better performance than the SVM standard

Оптимизация стратегий перестрахования с использованием системы поддержки принятия решений

Мета роботи полягає у дослідженні існуючих підходів до перестрахування, спрямованому на моделювання розподілу і мінімізацію ризику страхового портфеля, а також формування стратегії його оптимального перестрахування з використанням системи підтримки прийняття рішень (СППР). Запропоновано метод знаходження оптимальної стратегії перестрахування. Для цього вибрано статистичні моделі, що відповідають структурі, розміру та кількості збитків страхового портфеля, а також побудовано імітаційну модель сукупного страхового збитку. При знаходженні варіанта оптимального перестрахування враховано залежність коефіцієнта навантаження від форми перестрахування. Коефіцієнт навантаження враховано при розрахунку премії, а при порівнянні різних форм перестрахування використано однакові значення цього коефіцієнта. Виконано числове дослідження залежності оптимальної форми перестрахування від змінного коефіцієнта навантаження. Встановлено, що при врахуванні змінного коефіцієнта навантаження за певних значень капіталу, яким готова ризикнути страхова компанія, варіант stop-loss дає гірші результати, ніж інші форми перестрахування. Розроблено архітектуру, функціональну схему, а також програмне забезпечення СППР для розв’язання задачі оптимізації перестрахування (програмна платформа C#). Проілюстровано функціонування СППР, яка може забезпечити бізнес-аналітика критеріями для керівництва при прийнятті рішення стосовно вибору форми перестрахування страхового портфеля.The basic purpose of the work is a study of existing approaches to reinsurance directed towards modeling of distribution and minimization of risk for an insurance portfolio, and forming a strategy for its optimal reinsurance using developed decision support system. A method for a search of optimal reinsurance strategy is proposed. For this purpose statistical models were selected that correspond to the structure and volume of portfolio losses as well as the number of these losses. The simulation model for the total insurance losses is developed. While finding an optimal reinsurance strategy it was taken into consideration the dependence of the load coefficient on a specific form of reinsurance. A numerical study of the dependence between optimal reinsurance strategy and the varying load coefficient has been performed. It was established that taking into consideration of the variable load coefficient for specific risk capital values for an insurance company the stop-loss strategy provides worse results than other forms considered. An architecture and the functional layout for decision support system are proposed, and appropriate software was developed in C#. The DSS functioning has been illustrated on simulated example. The system will provide a useful instrument for a business analytic to support decision making while selecting a strategy for insurance portfolio.Цель работы заключается в исследовании существующих подходов к перестрахованию, направленному на моделирование распределения и минимизацию риска страхового портфеля, а также на формирование стратегии его оптимального перестрахования с использованием системы поддержки принятия решений (СППР). Предложен метод определения оптимальной стратегии перестрахования. Для этого выбраны статистические модели, которые соответствуют структуре, объему и количеству убытков страхового портфеля. При определении оптимального варианта перестрахования учтена зависимость коэффициента нагрузки от вида перестрахования. Коэффициент нагрузки учтен при расчете премии, а при сравнении различных форм перестрахования использованы одинаковые значения этого коэффициента. Выполнено численное исследование зависимости оптимальной формы перестрахования от переменного коэффициента нагрузки. Установлено, что с учетом переменного коэффициента нагрузки при определенных значениях капитала, которым готова рискнуть страховая компания, вариант stop-loss дает худшие результаты, чем другие формы перестрахования. Разработаны архитектура, функциональная схема, а также программное обеспечение СППР для решения задачи оптимизации перестрахования (программная платформа С#). Проиллюстрировано функционирование СППР, которая может обеспечить бизнес-аналитика критериями для руководства при принятии решений касательно выбора формы перестрахования страхового портфеля

Applying machine learning for healthcare: A case study on cervical pain assessment with motion capture

Given the exponential availability of data in health centers and the massive sensorization that is expected, there is an increasing need to manage and analyze these data in an effective way. For this purpose, data mining (DM) and machine learning (ML) techniques would be helpful. However, due to the specific characteristics of the field of healthcare, a suitable DM and ML methodology adapted to these particularities is required. The applied methodology must structure the different stages needed for data-driven healthcare, from the acquisition of raw data to decision-making by clinicians, considering the specific requirements of this field. In this paper, we focus on a case study of cervical assessment, where the goal is to predict the potential presence of cervical pain in patients affected with whiplash diseases, which is important for example in insurance-related investigations. By analyzing in detail this case study in a real scenario, we show how taking care of those particularities enables the generation of reliable predictive models in the field of healthcare. Using a database of 302 samples, we have generated several predictive models, including logistic regression, support vector machines, k-nearest neighbors, gradient boosting, decision trees, random forest, and neural network algorithms. The results show that it is possible to reliably predict the presence of cervical pain (accuracy, precision, and recall above 90%). We expect that the procedure proposed to apply ML techniques in the field of healthcare will help technologists, researchers, and clinicians to create more objective systems that provide support to objectify the diagnosis, improve test treatment efficacy, and save resources

Demand for Micro Life Insurance in Sri Lanka: Impact of Social Capital and Religion

Micro-life insurance provides protection against small premiums to low-income people in developing countries. Demand, however, is very moderate. The aim of this empirical work is to explain, how social capital and religion affect life insurance demand in developing countries using the example of Sri Lanka. Social networks allow for access to information, money, or innovation in an environment where infrastructure is not well functioning or less developed. Thus, social networks shape both, the consumption and the risk management behavior of individuals. In addition, cross-country studies show that the religion of Islam has a negative impact on life insurance consumption. This research follows a mixed-method research approach to study the role of social capital and religion on micro life insurance demand. The qualitative focus group discussions and the quantitative household surveys were conducted in the Eastern province of Sri Lanka in 2013. This work identifies three mechanisms through which social capital influences the demand for micro-life insurance: imitation, information exchange, informal risk sharing. People buy a micro-life insurance if they know an insured person. Informal risk management practices crowd-out formal micro-life insurance and the exchange between friends, family members or neighbors can reduce consumption if prior negative experiences weaken the confidence in the insurance promise. Regarding religion, the qualitative study shows that Muslims are reluctant to buy conventional insurance as they perceived it as a financial product contradicting with their religion. This study confirms that the financial situation contributes significantly to the purchase decision. It further found, that people are motivated to sign-up for micro life insurance by the perception to support other people in need with their purchase

UNDERSTANDING THE IMPACT OF RACISM IN HEALTHCARE AND HOW IT IS AFFECTING AFRICAN AMERICAN WOMEN (PART 2)

Healthcare is the most sacred part of every person’s life and should be accessible. However, the experience for many is that “you either got it or you don’t”. A way to help “get it”[Healthcare] is through workplace insurance, however, this is not accessible to many. This is especially the case for minority communities who cannot afford insurance plans, if and when offered by their employers, or other alternatives for accessing affordable health care. The terms “affordable and health care” are a paradox, for African American women. This is because of a system that was built to benefit one race but truly affects others. These particular guidelines in place make it very difficult to obtain coverage for basic needs, rendering Black women to make a decision that may affect her life or the family she is trying to create. The purpose of this session is to report on exploratory research using 10 published articles regarding African American women’s experience with the health care system. Research regarding their experience varies from personal stories to data surrounding similar death stories. Based on our initial exploration, there appears to be implicit and explicit bias against African American women, especially in prenatal and post-partum care. The birth-death rate for African women is growing at an alarming rate and people of color want to understand why. To support that statement there is data from the CDC stating that the pregnancy-related mortality ratio of African American women older than 30 was four to five times higher than it was for white women (CDC, 2019). Using data from Cobb and Douglas Public Health in the suburb of Atlanta, Georgia, is confirming the fact that insurance is a huge problem for receiving care. The connection leads to the problems many African Americans go through just to receive basic care with the high mortality death rate of pregnant African American women as an example

Statistical Discrimination in Labor Markets: An Experimental Analysis

Statistical discrimination occurs when distinctions between demographic groups are made on the basis of real or imagined statistical distinctions between the groups. While such discrimination is legal in some cases (e.g., insurance markets), it is illegal and/or controversial in others (e.g., racial profiling and gender-based labor market discrimination). First moment statistical discrimination occurs when, for example, female workers are offered lower wages because females are perceived to be less productive, on average, than male workers. Second moment discrimination occurs when risk averse employers offer female workers lower wages based not on lower average productivity but on a higher variance in their productivity. Empirical work on statistical discrimination is hampered by the difficulty of obtaining suitable data from naturally-occurring labor markets. This article reports results from controlled laboratory experiments designed to study second moment statistical discrimination in a simulated labor marker setting. Since decision-makers may not view risk in the same way as economists or statisticians (i.e., risk=variance of distribution), we also examine two possible alternative measures of risk: the support of the distribution, and the probability of earning less than the expected (maximum) profits for the employer. Our results indicate that individuals do respond to these alternative measures of risk, and employers made statistically discriminatory wage offers consistent with loss-aversion in our full sample (though differences between male and female employers can be noted). If one can transfer these results outside of the laboratory, they indicate that labor market discrimination based only on first moment discrimination is biased downward. The public policy implication is that efforts and legislation aimed at reducing discrimination of various sorts face an additional challenge in trying to identify and limit relatively hidden, but significant, forms of statistical discrimination.