The major goal of decisions made by a business organization is to enhance business performance. These days, owners, managers and other stakeholders are seeking for opportunities of modelling and automating decisions by analysing the most recent data with the help of artificial intelligence (AI). This study outlines a simple theoretical model framework using internal and external information on current and potential clients and performing calculations followed by immediate updating of contracting probabilities after each sales attempt. This can help increase sales efficiency, revenues, and profits in an easily programmable way and serve as a basis for focusing on the most promising deals customising personal offers of best-selling products for each potential client. The search for new customers is supported by the continuous and systematic collection and analysis of external and internal statistical data, organising them into a unified database, and using a decision support model based on it. As an illustration, the paper presents a fictitious model setup and simulations for an insurance company considering different regions, age groups and genders of clients when analysing probabilities of contracting, average sales and profits per contract. The elements of the model, however, can be generalised or adjusted to any sector. Results show that dynamic targeting strategies based on model calculations and most current information outperform static or non-targeted actions. The process from data to decision-making to improve business performance and the decision itself can be easily algorithmised. The feedback of the results into the model carries the potential for automated self-learning and self-correction. The proposed framework can serve as a basis for a self-sustaining artificial business intelligence system.

In this paper, we examine a possible application of ordered weighted average (OWA for short) aggregation operators in the insurance industry. Aggregation operators are essential tools in decision-making when a single value is needed instead of a couple of features. Information aggregation necessarily leads to information loss, at least to a specific extent. Whether we concentrate on extreme values or middle terms, there can be cases when the most important piece of the puzzle is missing. Although the simple or weighted mean considers all the values there is a drawback: the values get the same weight regardless of their magnitude. One possible solution to this issue is the application of the so-called Ordered Weighted Averaging (OWA) operators. This is a broad class of aggregation methods, including the previously mentioned average as a special case. Moreover, using a proper parameter (the so-called orness) one can express the risk awareness of the decision-maker. Using real-life statistical data, we provide a simple model of the decision-making process of insurance companies. The model offers a decision-supporting tool for companies.

In this study, we explore the impact of contemporary bank run incidents on stock market performance, taking into consideration insured deposit concentration. Specifically, we use data from the recent downfall of the Silicon Valley Bank (SVB). By employing event study methods with the mean-adjusted return model and market models, we evaluate the cumulative abnormal returns (CARs). Our findings reveal a substantial negative CAR for all the listed companies in our sample, suggesting that the SVB crisis adversely affected stock returns. Further analysis shows an even more pronounced effect on the banking sector and that banks with a high concentration of insured deposits experienced economically and statistically less negative CARs. We also find that the response by the Treasury Department, the Federal Reserve, the Federal Deposit Insurance Corporation, and other agencies—aimed at fully safeguard all depositors—led a rebound in CARs. Our results highlight the importance of deposit insurance policy and regulatory responses in protecting the financial system during panic events.

The effectiveness and efficiency of e-learning system in industry significantly depend on users’ acceptance and adoption. This is specifically determined by external and internal factors represented by subjective norms (SN) and experience (XP), both believed to affect users’ perceived usefulness (PU) and perceived ease of use (PEOU). Users’ acceptance of e-learning system is influenced by the immensity of region, often hampered by inadequate infrastructure support. Therefore, this study aimed to investigate behavioral intention to use e-learning in the Indonesian insurance industry by applying Technology Acceptance Model (TAM). To achieve this objective, Jabotabek and Non-Jabotabek regions were used as moderating variables in all related hypotheses. An online survey was conducted to obtain data from 800 respondents who were Indonesian insurance industry employees. Subsequently, Structural Equation Model (SEM) was used to evaluate the hypotheses, and Multi-Group Analysis (MGA) to examine the role of region. The results showed that out of the seven hypotheses tested, only one was rejected. Furthermore, XP had no significant effect on PU, and the most significant correlation was found between PEOU and PU. In each relationship path model, the role of region (Jabodetabek and Non Jabodetabek) had no significant differences. These results were expected to provide valuable insights into the components of e-learning acceptability for the development of a user-friendly system in the insurance industry.

The decentralization of the NHIS’s implementation to states intended to hasten progress towards universal health coverage, has not effectively addressed healthcare disparities, particularly in Lagos State. The implementation of the Lagos State Health Insurance Scheme appears to perpetuate structural violence, evident in increased out-of-pocket expenses, discrimination based on insurance type, and substandard healthcare delivery. The study therefore examined how structural violence has affected the policy outcomes of the Lagos State Health Insurance Scheme, with a specific emphasis on junior officers in grade level 01–07 in five selected ministries situated within Lagos State. Both primary and secondary data were collected using questionnaire, interview and literature search. Data gathered were analysed statistically and thematically. The findings of the study indicate that the policy outcome of the scheme has been adversely affected by structural violence, resulting in dissatisfaction, compensation claims for unresolved health issues and a shift in health insurance providers among enrolled junior officers.