Hasil untuk "Insurance"

C. Bernstein, J. Blanchard, E. Kliewer et al.

M. van den Akker, F. Buntinx, J. Metsemakers et al.

N. Adler, W. Boyce, M. A. Chesney et al.

Naciye Güliz Uğur, Adem Akbıyık

The tourism industry was one of the world's greatest markets; until the world met a pandemic in the 21st century, COVID-19. This study aims to present the reactions of travelers during the pandemic trends outlined by adopting text mining techniques. Between December 30, 2019–March 15, 2020, approximately 75,000 comments are retrieved from the TripAdvisor forums, and 23,515 cases from the US, Europe, and Asia forums are employed for analyses. The results reveal that the tourism sector is easily affected by global crises. It is almost the same day that travelers decide to cancel or delay their trips, with the spread of the news. More in-depth analyses uncovered several topics consisted of comments on benefiting from travel insurance and refund due to the travel cancellations. Travel insurance has become a hot topic, which may be a way of reanimating the industry by offering travel packages, including travel insurance services.

R. Busse, M. Blümel

P. Grossi, H. Kunreuther, C. Patel

Frank Cremer, Barry Sheehan, M. Fortmann et al.

Cybercrime is estimated to have cost the global economy just under USD 1 trillion in 2020, indicating an increase of more than 50% since 2018. With the average cyber insurance claim rising from USD 145,000 in 2019 to USD 359,000 in 2020, there is a growing necessity for better cyber information sources, standardised databases, mandatory reporting and public awareness. This research analyses the extant academic and industry literature on cybersecurity and cyber risk management with a particular focus on data availability. From a preliminary search resulting in 5219 cyber peer-reviewed studies, the application of the systematic methodology resulted in 79 unique datasets. We posit that the lack of available data on cyber risk poses a serious problem for stakeholders seeking to tackle this issue. In particular, we identify a lacuna in open databases that undermine collective endeavours to better manage this set of risks. The resulting data evaluation and categorisation will support cybersecurity researchers and the insurance industry in their efforts to comprehend, metricise and manage cyber risks.

M. Fafchamps, F. Gubert

I. Chen, Peter Szolovits, M. Ghassemi

Background As machine learning becomes increasingly common in health care applications, concerns have been raised about bias in these systems' data, algorithms, and recommendations. Simply put, as health care improves for some, it might not improve for all. Methods Two case studies are examined using a machine learning algorithm on unstructured clinical and psychiatric notes to predict intensive care unit (ICU) mortality and 30-day psychiatric readmission with respect to race, gender, and insurance payer type as a proxy for socioeconomic status. Results Clinical note topics and psychiatric note topics were heterogenous with respect to race, gender, and insurance payer type, which reflects known clinical findings. Differences in prediction accuracy and therefore machine bias are shown with respect to gender and insurance type for ICU mortality and with respect to insurance policy for psychiatric 30-day readmission. Conclusions This analysis can provide a framework for assessing and identifying disparate impacts of artificial intelligence in health care.

S. Mansi, William F. Maxwell, Darius P. Miller

Xin Huang, Hao Zhou, Haibin Zhu

In this paper we propose a framework for measuring and stress testing the systemic risk of a group of major financial institutions. The systemic risk is measured by the price of insurance against financial distress, which is based on ex ante measures of default probabilities of individual banks and forecasted asset return correlations. Importantly, using realized correlations estimated from high-frequency equity return data can significantly improve the accuracy of forecasted correlations. Our stress testing methodology, using an integrated micro-macro model, takes into account dynamic linkages between the health of major U.S. banks and macrofinancial conditions. Our results suggest that the theoretical insurance premium that would be charged to protect against losses that equal or exceed 15 percent of total liabilities of 12 major U.S. financial firms stood at $110 billion in March 2008 and had a projected upper bound of $250 billion in July 2008.

K. Couch, Dana W. Placzek

Stephen Petterson, W. Liaw, R. Phillips et al.

PURPOSE We sought to project the number of primary care physicians required to meet US health care utilization needs through 2025 after passage of the Affordable Care Act. METHODS In this projection of workforce needs, we used the Medical Expenditure Panel Survey to calculate the use of office-based primary care in 2008. We used US Census Bureau projections to account for demographic changes and the American Medical Association's Masterfile to calculate the number of primary care physicians and determine the number of visits per physician. The main outcomes were the projected number of primary care visits through 2025 and the number of primary care physicians needed to conduct those visits. RESULTS Driven by population growth and aging, the total number of office visits to primary care physicians is projected to increase from 462 million in 2008 to 565 million in 2025. After incorporating insurance expansion, the United States will require nearly 52,000 additional primary care physicians by 2025. Population growth will be the largest driver, accounting for 33,000 additional physicians, while 10,000 additional physicians will be needed to accommodate population aging. Insurance expansion will require more than 8,000 additional physicians, a 3% increase in the current workforce. CONCLUSIONS Population growth will be the greatest driver of expected increases in primary care utilization. Aging and insurance expansion will also contribute to utilization, but to a smaller extent.

Gabriel Felbermayr, J. Gröschl

Growth theory predicts that natural disasters should, on impact, lower GDP per capita. However, the empirical literature does not offer conclusive evidence. Most existing studies use disaster data drawn from damage records of insurance companies. We argue that this may lead to estimation bias as damage data and the selection into the database may correlate with GDP. We build a comprehensive database of disaster events and their intensities from primary geophysical and meteorological information. In contrast to insurance data, our GeoMet data reveal a substantial negative and robust average impact effect of disasters on growth. The worst 5% disaster years come with a growth damage of at least 0.45 percentage points. That average effect is driven mainly by very large earthquakes and some meteorological disasters. Poor countries are more strongly affected by geophysical disasters; rich more by meteorological events. International openness and democratic institutions reduce the adverse effect of disasters.

Molly Frean, J. Gruber, B. Sommers

Using premium subsidies for private coverage, an individual mandate, and Medicaid expansion, the Affordable Care Act (ACA) has increased insurance coverage. We provide the first comprehensive assessment of these provisions' effects, using the 2012-2015 American Community Survey and a triple-difference estimation strategy that exploits variation by income, geography, and time. Overall, our model explains 60% of the coverage gains in 2014-2015. We find that coverage was moderately responsive to price subsidies, with larger gains in state-based insurance exchanges than the federal exchange. The individual mandate's exemptions and penalties had little impact on coverage rates. The law increased Medicaid among individuals gaining eligibility under the ACA and among previously-eligible populations ("woodwork effect") even in non-expansion states, with no resulting reductions in private insurance. Overall, exchange premium subsidies produced 40% of the coverage gains explained by our ACA policy measures, and Medicaid the other 60%, of which 1/2 occurred among previously-eligible individuals.

R. Burby

Jing Cai, A. Janvry, E. Sadoulet

E. Benami, Zhenong Jin, M. Carter et al.

Bo Becker, Bo Becker, V. Ivashina et al.

Reaching-for-yield--investors' propensity to buy riskier assets in order to achieve higher yields--is believed to be an important factor contributing to the credit cycle. This paper presents a detailed study of this phenomenon in the corporate bond market. We show that insurance companies, the largest institutional holders of corporate bonds, reach for yield in choosing their investments. Consistent with lower rated bonds bearing higher capital requirement, insurance firms' prefer to hold higher rated bonds. However, conditional on credit ratings, insurance portfolios are systematically biased toward higher yield, higher CDS bonds. Reaching-for-yield exists both in the primary and the secondary market, and is robust to a series of bond and issuer controls, including bond liquidity and duration, and issuer fixed effects. This behavior is related to the business cycle, being most pronounced during economic expansions. It is also more pronounced for firms with poor corporate governance and for which regulatory capital requirement is more binding. A comparison of the ex-post performance of bonds acquired by insurance companies shows no outperformance, but higher systematic risk and volatility.

Anol Bhattacherjee, Chieh-Peng Lin