Solvency Analysis by Business Classifications of General Insurance Industry in Malaysia

A healthy and well developed insurance industry will improve the stability of an economy by transferring risks to various parties through insurance and reinsurance activities. Insurance companies’ performances have direct impact on the public welfare, to the regulatory authorities and to potential investors. Insolvency within the insurance industry has become a major concern and identification of potentially troubled firms has become a major regulatory research objective. The regulatory authority of the insurance industry in Malaysia has made compulsory the employment of risk-based capital (RBC) on the insurance industry with the objective to measure the minimum amount of capital required by an insurance company to ensure the continuous solvency and smooth running of the insurer's business operations. The measurement of RBC known as the capital adequacy ratio (CAR) must be adopted by each insurance company. However, different methods are practiced by other countries in allocating RBC. The objective of this study is to evaluate the performance of different insurance policies offered by the general insurance companies using the RBC system enforced by BNM and the RBC based on Butsic method. Study uses annual financial reports data of nineteen general insurance companies in Malaysia from 2009 to 2015. Results of the capital adequacy ratio (CAR) and expected policy deficit (EPD) found that the personal accident policy to be the most profitable and ensure solvency for the general insurance companies

A Comprehensive Survey on Enterprise Financial Risk Analysis: Problems, Methods, Spotlights and Applications

Enterprise financial risk analysis aims at predicting the enterprises' future financial risk.Due to the wide application, enterprise financial risk analysis has always been a core research issue in finance. Although there are already some valuable and impressive surveys on risk management, these surveys introduce approaches in a relatively isolated way and lack the recent advances in enterprise financial risk analysis. Due to the rapid expansion of the enterprise financial risk analysis, especially from the computer science and big data perspective, it is both necessary and challenging to comprehensively review the relevant studies. This survey attempts to connect and systematize the existing enterprise financial risk researches, as well as to summarize and interpret the mechanisms and the strategies of enterprise financial risk analysis in a comprehensive way, which may help readers have a better understanding of the current research status and ideas. This paper provides a systematic literature review of over 300 articles published on enterprise risk analysis modelling over a 50-year period, 1968 to 2022. We first introduce the formal definition of enterprise risk as well as the related concepts. Then, we categorized the representative works in terms of risk type and summarized the three aspects of risk analysis. Finally, we compared the analysis methods used to model the enterprise financial risk. Our goal is to clarify current cutting-edge research and its possible future directions to model enterprise risk, aiming to fully understand the mechanisms of enterprise risk communication and influence and its application on corporate governance, financial institution and government regulation

Predicting business failure using artificial intelligence system

This thesis was submitted for the award of Doctor of Philosophy and was awarded by Brunel University LondonPredicting business insolvency is considered one of the main supportive sources of information for decision making for financial institutions, investors, creditors, and other participants in the business market. Financial reporting systems provide relevant information that can be used to assess the financial position of firms. It is crucial to have classification and prediction models that can analyse this financial information and provide accurate assurance for users about business health. Recent studies have explored the use of machine learning tools as substitute for traditional statistical methods to develop classification models to classify firm insolvency according to financial statement information. However, these models have no ideal classifier, since each provides a certain percentage of wrong outputs, which is a crucial consideration; every percentage of wrong response can mean massive financial losses for stakeholders. Therefore, this study proposes new insolvency classification and perdition models based on machine learning modelling techniques to develop an improved classifier. Individual modelling techniques using statistical methods and machine learning were used to develop the classification model of business insolvency. The results showed that machine learning method outperformed statistical methods. Deep Learning (DPL) achieved the highest performance based on all performance measurements used in the study, and it was the best individual classifier, with average accuracy of 97.2% using all-years dataset. Ensemble- Boosted Decision Tree classifier ranked second, followed by Decision Tree classifier. Thus, it has been proven that DPL modelling approach is useful for business insolvency classification. A key contribution in enhancing individual classifier outputs is the use of traditional combining methods with two new aggregation methods in business insolvency (Fuzzy Logic and Consensus Approach). The Consensus Approach showed the best improvement in the results of all individual classifiers with average accuracy of 97.7%, and it is considered the best classification method not only in comparison with individual classifiers, but also with traditional combiners. This study pioneers the development of a time series business insolvency prediction model with Big Data for UK businesses. The aim of the model is to provide early prediction about a business health. Three prediction models were developed based on Nonlinear Autoregressive with Exogenous Input models (NARX), Nonlinear Autoregressive Neural Network (NAR), and Deep Learning Time-series model (DPL-SA) and achieved average accuracy rates of 83.6%, 89.5%, and 91.35%, respectively. The results show relatively high performance in comparison with the best individual classifier (deep learning)

Analysis of insurance companies’ performance capital structure and soundness: evidence from the U.K. market

Since the UK insurance industry could be regarded as an essential contributor in both international and domestic economic strength, it is worth to gain the insights within the performance of the UK insurance industry and get acknowledged of how different internal or external factors might influence its behaviours

Efficiency measurement. A methodological comparison of parametric and non-parametric approaches.

The thesis examines technical efficiency using frontier efficiency estimation techniques from parametric and non-parametric approaches. Five different frontier efficiency estimation techniques are considered which are SFA, DFA, DEA-CCR, DEA-BCC and DEA-RAM. These techniques are then used on an artificially generated panel dataset using a two-input two-output production function framework based on characteristics of German life-insurers. The key contribution of the thesis is firstly, a study that uses simulated panel dataset to estimate frontier efficiency techniques and secondly, a research framework that compares multiple frontier efficiency techniques across parametric and non-parametric approaches in the context of simulated panel data. The findings suggest that, as opposed to previous studies, parametric and non-parametric approaches can both generate comparable technical efficiency scores with simulated data. Moreover, techniques from parametric approaches, i.e. SFA and DFA are consistent with each other whereas the same applies to non-parametric approaches, i.e. DEA models. The research study also discusses some important theoretical and methodological implication of the findings and suggests some ways whereby future research can enable to overcome some of the restrictions associated with current approaches

Measuring knowledge sharing processes through social network analysis within construction organisations

The construction industry is a knowledge intensive and information dependent industry. Organisations risk losing valuable knowledge, when the employees leave them. Therefore, construction organisations need to nurture opportunities to disseminate knowledge through strengthening knowledge-sharing networks. This study aimed at evaluating the formal and informal knowledge sharing methods in social networks within Australian construction organisations and identifying how knowledge sharing could be improved. Data were collected from two estimating teams in two case studies. The collected data through semi-structured interviews were analysed using UCINET, a Social Network Analysis (SNA) tool, and SNA measures. The findings revealed that one case study consisted of influencers, while the other demonstrated an optimal knowledge sharing structure in both formal and informal knowledge sharing methods. Social networks could vary based on the organisation as well as the individuals’ behaviour. Identifying networks with specific issues and taking steps to strengthen networks will enable to achieve optimum knowledge sharing processes. This research offers knowledge sharing good practices for construction organisations to optimise their knowledge sharing processes

The 45th Australasian Universities Building Education Association Conference: Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment, Conference Proceedings, 23 - 25 November 2022, Western Sydney University, Kingswood Campus, Sydney, Australia

This is the proceedings of the 45th Australasian Universities Building Education Association (AUBEA) conference which will be hosted by Western Sydney University in November 2022. The conference is organised by the School of Engineering, Design, and Built Environment in collaboration with the Centre for Smart Modern Construction, Western Sydney University. This year’s conference theme is “Global Challenges in a Disrupted World: Smart, Sustainable and Resilient Approaches in the Built Environment”, and expects to publish over a hundred double-blind peer review papers under the proceedings

Factors Influencing Customer Satisfaction towards E-shopping in Malaysia

Online shopping or e-shopping has changed the world of business and quite a few people have decided to work with these features. What their primary concerns precisely and the responses from the globalisation are the competency of incorporation while doing their businesses. E-shopping has also increased substantially in Malaysia in recent years. The rapid increase in the e-commerce industry in Malaysia has created the demand to emphasize on how to increase customer satisfaction while operating in the e-retailing environment. It is very important that customers are satisfied with the website, or else, they would not return. Therefore, a crucial fact to look into is that companies must ensure that their customers are satisfied with their purchases that are really essential from the ecommerce’s point of view. With is in mind, this study aimed at investigating customer satisfaction towards e-shopping in Malaysia. A total of 400 questionnaires were distributed among students randomly selected from various public and private universities located within Klang valley area. Total 369 questionnaires were returned, out of which 341 questionnaires were found usable for further analysis. Finally, SEM was employed to test the hypotheses. This study found that customer satisfaction towards e-shopping in Malaysia is to a great extent influenced by ease of use, trust, design of the website, online security and e-service quality. Finally, recommendations and future study direction is provided. Keywords: E-shopping, Customer satisfaction, Trust, Online security, E-service quality, Malaysia

Construction management abstracts : cumulative abstracts and indexes of journals in construction management, 1983-2000

The purpose of this document is to provide a single source of reference for every paper published in the journals directly related to research in Construction Management. It is indexed by author and keyword and contains the titles, authors, abstracts and keywords of every article from the following journals: • Building Research and Information (BRI) • Construction Management and Economics (CME) • Engineering, Construction and Architectural Management (ECAM) • Journal of Construction Procurement (JCP) • Journal of Construction Research (JCR) • Journal of Financial Management in Property and Construction (JFM) • RICS Research Papers (RICS) The index entries give short forms of the bibliographical citations, rather than page numbers, to enable annual updates to the abstracts. Each annual update will carry cumulative indexes, so that only one index needs to be consulted