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Insurance in Malta has been very largely influenced by English practice and law. The influence of the English market insurance practice and law not only shaped the Maltese market but practically that of all common law jurisdictions in former members of the British empire. Since the London insurance market continues to be a very dominant force globally until today, the connection has undoubtedly served Malta well.

The origins of UK insurance principles of utmost good faith and insurable interest under contract law, date back to times which were very different from today and the need to revise the laws has now been felt in the UK as well as in other jurisdictions which were influenced by its law and practice. In Malta, minimal legislative intervention and the Maltese courts were and continue to be mostly guided by English case law, some of which has now been superseded by the updated statute law which was recently introduced in the UK by virtue of the Consumer Insurance (Disclosure and Representations) Act (2012) and Insurance Act (2015).

We herein lay out a case study of the development of utmost good faith and insurable interest in insurance contracts within the Maltese legal context, based on empirical literature findings and semi-structured interviews together with several legal experts who are specialized in the field and experienced insurance professionals.

This study aims to investigate insurance policy loan applicant characteristics. Additionally, it reveals the behaviour patterns of heavy users who have applied for at least two loans. A policy loan prediction model is established which is designed to increase loan application rates.

The proposed model is implemented using data‐mining techniques and comprises two mechanisms: a business rule generator and a recommendation mechanism. Two analytical approaches, the C.5 and Apriori algorithm, are employed to analyse the profile and browsing log DBs of insured individuals. The prediction model is verified by actual data from a Taiwanese insurance company.

The data‐mining results reveal that five attributes are ultimately used to establish the prediction model, namely: gender, marketing channel, insurance type, area of policy owner, and assumed interest rate. Additionally, the analytical results also indicate that insured individuals apply for loans as a result of arbitrage inducement. The accuracy of loan applicant prediction can exceed 70 per cent. Finally, some interesting patterns emerge for heavy users, such as the finding that loan applicants are used to applying for loans continuously (loan application repetition is on average two to three times).

Some policy owners who are unfamiliar with the web interface prefer to contact insurance personnel directly to discuss their insurance needs, and thus no browsing records are available for such users. In such cases only the profile could be collected and analysed.

The proposed model enables insurance firms to locate potential loan applicants according to the data‐mining results. As in the illustration scenario in the paper, insurance personnel can contact these potential loan applicants before they submit loan applications to the bank. Additionally, loan‐related information is provided for online insurance users based on their browsing logs. The loan application rate is thus expected to increase, along with interest revenue.

As long as the policy proceeds, the interest income from the policy loan seems to be a good option for extending insurance company operational earnings. Understanding the characteristics of loan applicants will provide helpful information. Besides, the proposed mechanism will be more appropriate to online users, who are unwilling to deal with unwanted information.

The purpose of this paper is to contribute to the aforementioned literature on the linkage between economic activity and human preference by estimating the cross-sectional determinants of farmers’ participation in participation in crop insurance programs (CIPs) and identifying the impediments preventing the remaining farmers from participating.

Using the unique data sets of risk preference experiments and maize producer surveys pertaining to the maize production areas of China, this paper explores the determinants of farmers’ CIPs and scrutinizes the role of risk aversion in farmers’ CIP purchase decisions under the expected utility maximization framework. And a “non-zero threshold probit model” is used for the analysis.

The results show that risk aversion plays an important role in CIP purchase decision-making, not only in the form of its direct effect but also with regard to the interaction term and expected loss. Furthermore, if the insured amount is high enough, then risk aversion will no longer affect insurance purchase. Additionally, purchase experience, CIP environment (village purchase ratio), and contract items (insured amounts) are significant determinates in these decisions. There is no significant evidence to suggest that serious adverse selection exists in the sampling areas.

One theoretical model is established which considered not only general variables like farmers’ production and household information, but also conditions and terms in the insurance policies. The unique experimental method is used to measure farmers’ risk aversion. Both the role of risk aversion and its’ interaction terms with others in CIP participation are scrutinized to identify complicated influences under the context of real society.

Mr Hiron first explained the theory of insurance as the transference of the loss of the individual to the pockets of the many by means of the operation of a common fund or pool operated by insurance companies and underwriters. Interesting historical facts were explained and the strength of the British insurance market was emphasized by the statement that one of the largest risks in the world, the liner United States, is insured for £11,000,000, more than half of which is covered in the London market.

What should be insured, what can be insured, how best to insure it are things which are forever changing. ‘What if’ situations are limitless so I shall be concerned here only with insurance against damage to property and ignore consequential loss, liabilities, construction risks, obligations under leases and other aspects.

The purpose of this paper is to demonstrate how crop price insurance premium can be calculated using an option pricing model and how insurers can transfer underwriting risks in the futures market.

Based on data from spot and futures market in China, this paper develops an improved B-S model for the calculation of crop price insurance premium and tests the possibility of hedging underwriting risks by insurance firms in the futures market.

The authors find that spot price of crops in China can be estimated with agricultural commodity futures prices, and can be taken as the insured price for crop price insurance. The authors also find that improved B-S model yields better estimation of crop price insurance premium than traditional B-S model when spot price does not follow geometric Brownian motion. Finally, the authors find that hedging can be one good alternative for insurance firms to manage underwriting risks.

This paper develops an improved B-S model that is data-driven in nature. Insured price of the crop price insurance, or the exercise price used in the B-S model, is estimated from a co-integration model built on spot and futures market price series. Meanwhile, distributional patterns of spot price series, one important factor determining the applicability of B-S model, is factored into the improved B-S model so that the latter is more robust and friendly to data with varied distributions. This paper also verifies the possibility of hedging of underwriting risks by insurance firms in the futures market.

This paper examines the risk factors of medical expense in China and applies statistical models to analyze these factors. Adopting data from social and private health insurance, this paper discusses the application of some multi‐variable statistical models in analyzing the risk factors of medical service utilization and medical expense. This study concludes that while the medical service utilization rate mainly depends on some hazard characteristics of the insured, the main risk factors of medical expense come from doctors and hospitals. The study also shows that an analysis of risk factors is useful for the risk control in health insurance, but the statistical models should be used properly according to the type of data.

A recent study found state bond bank participants continually realize considerable interest cost savings. Savings were calculated as differences in interest costs of bond bank loans and the bond offerings participants would have sold as alternatives to loans, (alternative market offerings). The present evaluation determines the sources of the savings. Savings are generated by not only differences in issue characteristics of bond bank issues and alternative market offerings, but also differential impacts of the same market forces and institutional factors on the interest costs of both types of sales. These findings verify that bond bank issues and alternative market offerings sell in different sub-markets, and confirm municipal bond market segmentation.

Previous studies generally focused on the definition of cybercrime and its effect on the market. Following Kesan’s study, this paper aims to analyse the relationship between cyber insurance and social welfare and compare it among three countries, namely, USA, UK and Turkey. The paper also discusses the main obstacles that the cyber insurer has to deal with and its effect on social welfare. This paper answers two questions related to cyber insurance at an aggregate level. First, “what kind of contribution does cyber insurance make to social welfare?” Second,“What kind of problems do insurers and insured have to face?” Although the findings are similar to Kesan’s study, this study gives an opportunity to make a country-based study and interpret the results with a different perspective.

The calculation of utility is also important for interpreting social welfare in the market. Consumer behaviour under uncertainty constructs the background for this paper because the risks of malicious attacks are contingent and independent, which means that consumers have to make their decisions under uncertainty. Von-Neumann-Morgenstern utility function is used for interpreting consumer’s behaviour.

Basically, there are two important conclusions that can derive for cyber insurance. First, cyber insurance can be defined as a higher security investment when coupled with increased levels of safety and a robust IT infrastructure. Second, cyber insurance, as a high-security investment, would have a positive impact on social welfare by making the internet safer for all users. The results show that the problems that lead to market failure can be virtually eliminated with an accurate risk assessment that leads to appropriate premium levels for insured. These results are consistent with those of study by Kesan et al. (2006).

Data availability for different industries have limited the ability to compare the impact of cyber-crime to different sectors.

Technological devices have become part of our daily life. Although they have brought us increasing access to all types of information, including opportunities for business, they have also increased the risk of malicious attacks and the risk of e-crime. By replicating the economic model used by Kesan et al. (2006), social welfare losses and insurance premiums are calculated for three countries: USA, UK and Turkey. Questions pertaining to contribution of cyber insurance to social welfare and problems faced by insurers and insured are addressed.

ARRANGEMENT OF REGULATIONS PART I GENERAL