Chapter Two: Literature Review
Life insurance industry in Bangladesh is one the upcoming sectors where the government is putting an effort for development. In the recent years there has been a wide range of regulatory actions undertaken by the Insurance Development and Regulatory Authority (IDRA) to reform the sector with more transparency. As a member of the industry I particularly think that it is a very good initiative that the industry is advancing towards more customer awareness through better transparency. Furtherance to this one of the measures taken by IDRA in the recent past is that it has become mandatory for the life insurers to disclose a series of financial indicators including the data reconciliation in the annual report. Though this is a very benefitting initiative but I believe a better picture of the companies can be depicted through proper analysis of the relevant parameters or drivers which concerns the policyholders.
Through this paper I intend to carry out a detailed analysis of the relevant parameters which can depict a clearer picture for the stakeholders. Afore commencing my work I have tried to educate myself in regards to past and recent developments in this analysis of life insurance companies and while doing so I have learned that there have been many study and analysis in regards to life insurance companies’ capital structure, investment, efficiency, profitability, solvency and risk management. I am mentioning some of my learning below:
Sommer and Cummins (1996) investigated the capital and portfolio risk decisions of Property – Casualty and Life insurance firms. A positive relationship was found between the risk and insurer capital suggesting that firms must balance these two factors if they intend to achieve their desired overall insolvency risk. It was also proved in their work that managerial incentives play a role in determining capital and risk in insurance markets.
Paterson and Gaver (2007) analyzed the loss-reserving practices of 562 insurance companies in 1993 to assess the relation between client influence and auditor oversight. The findings were that financially struggling insurers tend to under- reserve, but this behavior is reduced when the weak insurer is important to the local practice office of the auditor.
Luhnen and Eling (2010) carried out an efficiency comparison with a sample of 6462 insurers from 36 countries. Their findings were a steady technical and cost efficiency growth in international insurance market from 2002 to 2006 with an existing large deviation across countries. Denmark and Japan showed the highest average efficiency, whereas Philippines has the least efficiency. As far as organizational forms are concerned, the results are not consistent with the expense preference hypothesis, which claims that mutual companies should be less efficient than stocks due to higher agency costs. Only minor variations are found when comparing different frontier efficiency methodologies (data envelopment analysis, stochastic frontier analysis).
Pottier (2007) in this paper the determinants of private debt holdings in the life insurance industry were examined. The study depicted that larger insurers i.e. insurers with higher financial quality mutual insurers, publicly traded insurers, mutual insurers, insurers with greater cash holdings and insurers facing stringent regulation are more prevalent lenders in the private debt market.
Carson, Hoyt and Liebenberg (2009) studied life insurance policy loan demand in term of four hypothesis that have been put forward in the literature. They examined aggregate data from the Survey of Consumer Finances that allow an alternative and in some cases more direct examination of policy loan demand based on individual household circumstances. The finding was a significant positive relation between loan demand and recent household expense or income shocks. By observing actual life insurance holdings and policy loan data for families, the authors provide evidence in support of the policy loan emergency fund hypothesis. The findings are particularly relevant for insurers since the results provide evidence of increase in policy loans following expense and /or income shocks at the household level. Such a finding is crucial for insurers as they account for the effects of economic conditions in their estimates in their estimates of policy loan demand. The results also suggest that credit scores may be useful predictors of loan demand.
Schulze, Grudl and Post (2006) analyze the implications of demographic risk on the optimal risk management mix (equity capital, asset allocation, and product policy) for a limited liability insurance company operating in a market with insolvency-averse insurance buyers. The results show that the utilization of natural hedging is optimal only if equity is scarce.
Jones and Kwon (2008) present a discrete-time, multi-state model for risk factor changes and mortality, which facilitates a more accurate description of mortality dynamics and quantification of variability in mortality. This model is extended to reflect health status and then used to analyze the impact of selective lapsation of life insurance policies and to predict mortality under re-entry term insurance.
Scott, Mansur, Elyasiani and Carson (2007) investigated the interest rate sensitivity of monthly stock returns of life insurers. Results based on data for the period 1975 through 2000 indicate that life insurer equity values are sensitive to long-term interest rates and that interest sensitivity varies across sub periods and across risk-based and size-based portfolios. The results complement insolvency research that links insurer financial performance to changes in interest rates.
The more established methods for quantifying operational risk are linear models such as time series models, econometric models, empirical actuarial models, and extreme value theory. Due to data limitations and complex interaction between operational risk variables, various non-linear methods have been proposed, one of which is Bayesian networks. Using an idealized example of a fictitious on line business, Cowell, Verrall, and Yoon (2007) construct a Bayesian network that models various risk factors and their combination into an overall loss distribution. Using this model, the authors showed how established Bayesian network methodology can be applied to: 1) form posterior marginal distributions of variables based on evidence, 2) simulate scenarios, 3) update the parameters of the model using data, and 4) quantify in real-time how well the model predictions compare to actual data.