THE LIFE INSURANCE COMPANY INCOME TAX ACT OF 1959: An Appraisal of the Effects upon the Life Insurance Industry: Part II

1964 ◽  
Vol 17 (1) ◽  
pp. 40-56
Author(s):  
STEPHEN M. KAUFMAN
Keyword(s):  
Life Insurance ◽  
Income Tax ◽  
Insurance Industry ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Life Insurance Industry

Ordnungspolitische Weichenstellungen für die anstehende Reform der Riester-Rente

2020 ◽  
Vol 69 (3) ◽  
pp. 239-249
Author(s):  
Axel Kleinlein
Keyword(s):  
Life Insurance ◽  
Financial Services ◽  
Service Providers ◽  
Insurance Industry ◽  
Financial Service ◽  
Mandatory Retirement ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Insurance Sector ◽  
Life Insurance Industry

Abstract The Riester pensions today face two main problems: First, life insurance industry in Germany faces the problem of inadequate solvency. Therefore, there is a need that we take the Riester pension not as a sole part of the life insurance sector and open it to the whole sector of financial services. Second, the previous regulation of the Riester pension is causing problems. Particularly the guarantee forces mandatory retirement with a life insurance company and the requirement of capital preservation. Therefore we have to review these two guarantee aspects. It is also important to limit costs and to simplify the funding system. The concept of the “Basisdepot-Vorsorge” solves these problems while it is based on promoting precisely those who want to save up for their retirement during their active career, no matter what kind of financial service is included in the accumulation or decumulation phase. To include all different financial service providers creates the needed economical competition to ensure better products for the Riester-Rente.

The China United Assurance Society and the Making of Chinese Life Insurance, 1912–1949

2020 ◽  
Vol 21 (3) ◽  
pp. 681-715 ◽  
Author(s):  
MATTHEW LOWENSTEIN
Keyword(s):  
Life Insurance ◽  
Insurance Industry ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Life Insurance Industry ◽  
History Of ◽  
Turbulent Process ◽  
Fierce Competition ◽  
Lapse Rates ◽  
Contemporary Chinese

This article traces the history of the first Chinese life insurance company: the China United Assurance Society. China United was founded in Shanghai in 1912 as a purely Chinese-owned enterprise and became the first Chinese life insurer to survive past its eighth year. By 1935, it boasted insurance in force of over 20 million yuan. In adapting life insurance to Republican China, China United had to contend with a number of extraordinary challenges. It had to train a corps of Chinese technical experts in a country without a single accredited actuary. It had to cultivate demand for a product that was poorly understood and often distrusted. At the same time, the Society was forced to find a way to manage a nationwide sales network that could market insurance products to a country that hitherto had little knowledge of life insurance. In doing so, it was threatened by interethnic strife sparked by racist practices of the foreign manager. Finally, China United had to overcome increasingly fierce competition, high lapse rates, and excess mortality that combined to drive underwriting profits negative. The Society was able to survive as a going concern only through its investing prowess in Chinese capital markets. Using previously unmined sources from the Shanghai Municipal Archives, this article charts China United’s turbulent process of indigenization, and explores its lasting legacies in the contemporary Chinese life insurance industry.

scholarly journals Transforming Underwriting in the Life Insurance Industry

2019 ◽  
Vol 33 ◽  
pp. 9373-9380 ◽  
Author(s):  
Marc Maier ◽  
Hayley Carlotto ◽  
Freddie Sanchez ◽  
Sherriff Balogun ◽  
Sears Merritt
Keyword(s):  
Life Insurance ◽  
Insurance Industry ◽  
Evaluation Framework ◽  
Insurance Companies ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Data Set ◽  
Life Score ◽  
Individual Level ◽  
Life Insurance Industry

Life insurance provides trillions of dollars of financial security for hundreds of millions of individuals and families worldwide. Life insurance companies must accurately assess individual-level mortality risk to simultaneously maintain financial strength and price their products competitively. The traditional underwriting process used to assess this risk is based on manually examining an applicant’s health, behavioral, and financial profile. The existence of large historical data sets provides an unprecedented opportunity for artificial intelligence and machine learning to transform underwriting in the life insurance industry. We present an overview of how a rich application data set and survival modeling were combined to develop a life score that has been deployed in an algorithmic underwriting system at MassMutual, an American mutual life insurance company serving millions of clients. Through a novel evaluation framework, we show that the life score outperforms traditional underwriting by 6% on the basis of claims. We describe how engagement with actuaries, medical doctors, underwriters, and reinsurers was paramount to building an algorithmic underwriting system with a predictive model at its core. Finally, we provide details of the deployed system and highlight its value, which includes saving millions of dollars in operational efficiency while driving the decisions behind tens of billions of dollars of benefits.

scholarly journals Measuring Efficiency of Life Insurance Instution in Indonesia: Data Envelopment Analysis Approach

2017 ◽  
Vol 5 (1) ◽  
pp. 012
Author(s):  
Aam S. Rusydiana ◽  
Taufiq Nugroho
Keyword(s):  
Data Envelopment Analysis ◽  
Life Insurance ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Data Envelopment ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Life Insurance Industry ◽  
Measuring Efficiency ◽  
Input Variables

This study aims to measure the level of efficiency of the life insurance industry in Indonesia. The calculation of the efficiency level in this study is relative, not absolute. The approach used is Data Envelopment Analysis (DEA). There are 8 research objects: Prudential, BNI Life, PaninDai-IchiLife, Asuransi Jiwasraya and Life Insurance Adisaranan Wanaartha, Takaful Takaful Insurance, Amanahjiwa Giri sharia insurance and Al-Amin sharia life insurance. This study consists of three input variables (cost of Commissive (X1), Operational Cost (X2), Total Equity (X3) and 2 output variables (Premium) (Y1) and Investment Revenue (Y2)). The results explain that there are 15 perfectly efficient DMUs (100%). And an inefficient of 24 DMU, consisting of 7 DMU conditions IRS and 17 DMU with DRS conditions. Of all the DMU observed, Prudential insurance is a life insurance company that is able to maintain its gradual efficiency level from 2013 to 2016 when compared to other life insurance in this observation. In general, the main factor inefficiency of life insurance industry in Indonesia (in observation) from 2012 to 2016 is from the output side. To be more efficient then life insurance companies should increase the value of premiums by 91% and investment income of 8%.

scholarly journals LIFE INSURANCE INDUSTRY IN INDIA-AN OVERVIEW

2016 ◽  
Vol 4 (10(SE)) ◽  
pp. 30-36
Author(s):  
N.Senthil Kumar ◽  
K. Selvamani
Keyword(s):  
Life Insurance ◽  
Insurance Industry ◽  
Insurance Companies ◽  
Effective Control ◽  
Insurance Company ◽  
Marine Insurance ◽  
Life Insurance Company ◽  
Insurance Sector ◽  
Life Insurance Industry ◽  
The Government

The first insurer of life was the marine insurance underwriters who started issuing life insurance policies on the life of master and crew of the ship, and the merchants. The first insurance policy was issued on 18th June 1583,on the life of WILLIAM GIBBONS for the period of 12 months. The oriental life insurance company is the first insurance companies in India which is started on 1818 by Europeans at Kolkata. The Indian Life Assurance Companies Act, 1912 was the first statutory measure to regulate life business. In 1928, the Indian Insurance Companies Act was enacted to enable the Government to collect statistical information about both life and non-life business transacted in India by Indian and foreign insurers including provident insurance societies. In 1938, with a view to protecting the interest of the Insurance public, the earlier legislation was consolidated and amended by the Insurance Act, 1938 with comprehensive provisions for effective control over the activities of insurers. In 1956 the life insurance companies was nationalized. The LIC absorbed 154 Indian, 16 non-Indian insurers as also 75 provident societies—245 Indian and foreign insurers in all. The LIC had monopoly till the late 90s when the Insurance sector was reopened to the private sector.

scholarly journals Review On Application of Data Mining in Life Insurance

2018 ◽  
Vol 7 (4.5) ◽  
pp. 159
Author(s):  
Vaibhav A. Hiwase ◽  
Dr. Avinash J Agrawa
Keyword(s):  
Risk Factors ◽  
Data Mining ◽  
Life Insurance ◽  
Insurance Industry ◽  
Feature Space ◽  
Machine Learning Techniques ◽  
High Dimensional ◽  
Insurance Company ◽  
Financial Loss ◽  
Life Insurance Company

The growth of life insurance has been mainly depending on the risk of insured people. These risks are unevenly distributed among the people which can be captured from different characteristics and lifestyle. These unknown distribution needs to be analyzed from        historical data and use for underwriting and policy-making in life insurance industry. Traditionally risk is calculated from selected     features known as risk factors but today it becomes important to know these risk factors in high dimensional feature space. Clustering in high dimensional feature is a challenging task mainly because of the curse of dimensionality and noisy features. Hence the use of data mining and machine learning techniques should experiment to see some interesting pattern and behaviour. This will help life insurance company to protect from financial loss to the insured person and company as well. This paper focuses on analyzing hidden correlation among features and use it for risk calculation of an individual customer.  

scholarly journals Most Efficient Fluctuation Reserves

1974 ◽  
Vol 8 (1) ◽  
pp. 66-76
Author(s):  
D. G. Halmstad
Keyword(s):  
New York ◽  
Life Insurance ◽  
Income Tax ◽  
Absolute Amount ◽  
Insurance Company ◽  
Life Insurance Company ◽  
Stock Company ◽  
Similar Nature ◽  
Minimum Capital ◽  
Group Life

In 1935 the New York Insurance Department introduced the concept of special contingency funds for certain types of insurance. Such requirements had first been introduced in 1925 for mutual workmen's compensation companies. Clear, consistent principles for these funds were not stated at the time, but their purpose seems to be to provide a cushion that may be used in time of serious financial difficulty.In group life insurance, this fund is a “special contingency reserve” and is carried at the suggestion of the New York Department). For mutual casualty, nonprofit hospitalization and medical indemnity plans, and for reciprocal insurers, the fund is treated as “special contingent surplus”, and is a mandated substitute for the minimum capital required of stock insurers). The U.S. federal Life Insurance Company Income Tax Act of 1959 recognizes special credits of a similar nature for health, non-participating life and group life and health insurance coverages.In all of these cases, the accumulations or credits are defined by a designated percentage of premiums). For the New York Department group life reserve and the Income Tax health and group life credit, a maximum is defined by a second percentage of the same annual premiums base. For the nonprofit plans' surplus, a similar maximum in terms of premium is used. However, in the case of mutual casualty companies, and of reciprocal insurers, the “special contingency surplus” must be built up to a defined absolute amount equal to the amount that would be required as capital of a stock company; there are no statutory provisions for withdrawing any part of such a fund once it is accumulated.

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