Accountability answers company insurance life based investing against the risk of failure to pay for policyholders

The problem of this research is how to regulate investment-based life insurance in Indonesia and the liability of investment-based life insurance companies against the risk of default by policyholders. This study uses a research method that has an empirical juridical type. The study results explain that the regulation of investment-based life insurance in Indonesia is regulated in Law Number 40 of 2014 concerning Business Per Insurance, OJK Regulation Number 23/POJK.05/2015 concerning Insurance Products and Marketing and Decree of the Chairman of BPPM and Financial Institutions Number KEP-104/ BL/2006 concerning Investment-based life insurance products. PP Number 87 of 2019 concerning insurance companies in the form of joint ventures, RI's Financial Decree Number 422/KMK.06/2003 and Director General of Financial Institutions Decree Number 2475/LK concerning investment insurance products and forms of liability of default insurance companies must fulfill the contents of the agreement insurance that gives rise to the rights and obligations of the insured reciprocally. However, Law Number 40 of 2014 concerning Insurance Business does not fully regulate violations in the insurance business and does not regulate how the insurance company is responsible for the company's inability to fulfill insurance claims.

Automobile Fatal Accident and Insurance Claim Analysis Through Artificial Neural Network

This chapter presents a thorough descriptive analysis of automobile fatal accident and insurance claims data. Major components of the artificial neural network (ANN) are discussed, and parameters are investigated and carefully selected to ensure an efficient model construction. A prediction model is constructed through ANN as well as generalized linear model (GLM) for model comparison purposes. The authors conclude that ANN performs better than GLM in predicting data for automobile fatalities data but does not outperform for the insurance claims data because automobile fatalities data has a more complex data structure than the insurance claims data.

Nationwide Comprehensive Epidemiological Study of Rare Diseases in Japan Using a Health Insurance Claims Database

BackgroundMore than 7,000 diseases constitute what are called rare diseases, and they mostly have no specific treatment. Disease profiles, such as prevalence and natural history, among the population of a specific country are essential in determining for which disease to research and develop drugs. In Japan, disease profiles of fewer than 2,000 rare diseases, called Nanbyo, have been investigated. However, non-Nanbyo rare diseases remain largely uninvestigated. Accordingly, we reveal the prevalence and natural history of rare diseases among the Japanese population, using the National Database of Health Insurance Claims and Specific Health Checkups of Japan, which covered 99.9% of public health insurance claims from hospitals and 97.9% from clinics as of May 2015. Then, we compared them with the data reported in Orphanet. This cross-disease study is the first to analyze rare-disease epidemiology in Japan with high accuracy, disease coverage, and granularity.ResultsWe were provided with the number of patients of approximately 4,500 rare diseases by sex and age for 10 years with the permission of the Ministry of Health, Labour and Welfare. About 3,000 diseases have equivalent terms in Orphanet and other medical databases. The data show that even if the Nanbyo systems do not cover a rare disease, its patients survive in many cases. Moreover, regarding natural history, genetic diseases tend to be diagnosed later in Japan than they are in the West. The data collected for this research work are available in the supplement and the website of NanbyoData.ConclusionsOur research work revealed the basic epidemiology and the natural history of Japanese patients with rare diseases using a health insurance claims database. The results imply that the coverage of the present Nanbyo systems is inadequate for rare diseases. Therefore, fundamental reform might be needed to reduce unfairness between rare diseases. Moreover, most diseases in Japan follow a tendency similar to those reported in Orphanet. However, some diseases are detected later, partly because fewer clinical genetic tests are available in Japan than there are in the West. Finally, we hope that our data and analysis accelerate drug discovery for rare diseases in Japan.

IMPROVING AUTOMOBILE INSURANCE CLAIMS FREQUENCY PREDICTION WITH TELEMATICS CAR DRIVING DATA

Novel navigation applications provide a driving behavior score for each finished trip to promote safe driving, which is mainly based on experts’ domain knowledge. In this paper, with automobile insurance claims data and associated telematics car driving data, we propose a supervised driving risk scoring neural network model. This one-dimensional convolutional neural network takes time series of individual car driving trips as input and returns a risk score in the unit range of (0,1). By incorporating credibility average risk score of each driver, the classical Poisson generalized linear model for automobile insurance claims frequency prediction can be improved significantly. Hence, compared with non-telematics-based insurers, telematics-based insurers can discover more heterogeneity in their portfolio and attract safer drivers with premiums discounts.

A Strategy to Identify Event Specific Hospitalizations in Large Health Claims Databases

Background Health insurance claims data offer a unique opportunity to study disease distribution on a large scale. Challenges arise in the process of accurately analyzing these raw data. One important challenge to overcome is the accurate classification of study outcomes. For example, using claims data, there is no clear way of classifying hospitalizations due to a specific event. This is because of the inherent disjointedness and lack of context that typically come with raw claims data. Methods In this paper, we propose a framework for classifying hospitalizations due to a specific event. Results We then test this framework in a health insurance claims database with approximately 4 million US adults who tested positive with COVID-19 between March and December 2020. Our claims specific COVID-19 related hospitalizations proportion is then compared to nationally reported rates from the Centers for Disease Control by age and sex. Conclusions The proposed methodology is a rigorous way to define event specific hospitalizations in claims data. This methodology can be extended to many different types of events and used on a variety of different types of claims databases.