Enhancing financial security through machine learning: Adoption challenges in Jordan’s insurance fraud detection

Performance management (PM) is a common practice used by organizations to assess and manage employees’ work. Much of PM research is closely related to management practices. Corporations in the public and nonprofit sector continuously develop PM programs to ensure the sustainability of their organizations.
The study aims to analyze the impact of strategic human resources planning on the organizational performance of Jordanian public shareholding companies for senior management and functional unit managers (human resources, marketing, finance, and accounting). The researchers surveyed all the public shareholding companies registered with the Jordan Securities Commission (JSC) in 2019, wherein they found that only 60 companies applied strategic planning and human resources planning (HRP) together. Two hundred and twenty questionnaires were distributed in 52 companies surveyed, and 203 were adopted for statistical analysis. Several statistical methods were used, most notably the multiple regression analysis. The researchers found out a statistically significant impact of the strategic human resources planning (integration of HRP and strategic planning; strategic participation) on organizational performance. The results showed that adopting the strategic HRP dimensions leads to an increase in an organization’s overall productivity, employee satisfaction and reputation, as well as reduced operating costs. HR managers must understand the effectiveness of strategically designed HR practices across functions.

The hotel industry is critical in developing the economy. Moreover, it is the largest and most rapidly growing industry in Jordan. Employee satisfaction is a crucial element for the success of any organization, particularly in the hotel industry. Therefore, this study aims to examine the influence of human resource management (HRM) practices since these constructs could influence different outcomes at the workplace, such as employee satisfaction, service quality, and employee service behavior in the hotel industry. The study has undertaken five primary HRM practices – recruitment, capability, compensation, performance appraisal, and training and development (T&D) – to measure their impact on employee satisfaction, service quality, and employee service behavior. Data were collected from 290 employees and 290 customers of Jordan’s hotels across all categories in the four main tourist attractions: Amman, Petra, Aqaba, and the Dead Sea. A quantitative approach was employed using various statistical tools such as mean, tabulation of data, correlation, and ANOVA by SPSS software. The results indicated that HRM practices positively affect service quality, employee service behavior, and employee satisfaction. It was also found that when employees in Jordan’s hotel industry demonstrate excellent service behavior, the customer perceptions of service quality increase. Finally, effective human resource management strategy systematically organizes all individual human resource management measures to directly influence employee satisfaction, service behavior, and service quality in a way that leads hotels to achieve organizational success.

The rise of robo-advisor wealth management services, which constitute a key element of fintech revolution, unveils the question whether they can dominate human-based advice, namely how to address the client’s behavioral biases in an automated way. One approach to it would be the application of machine learning tools during client profiling. However, trained neural network is often considered as a black box, which may raise concerns from the customers and regulators in terms of model validity, transparency, and related risks. In order to address these issues and shed more light on how neurons work, especially to figure out how they perform computation at intermediate layers, this paper visualizes and estimates the neurons’ sensitivity to different input parameters. Before it, the comprehensive review of the most popular optimization algorithms is presented and based on them respective data set is generated to train convolutional neural network. It was found that selected hidden units to some extent are not only specializing in the reaction to such features as, for example, risk, return or risk-aversion level but also they are learning more complex concepts like Sharpe ratio. These findings should help to understand robo-advisor mechanics deeper, which finally will provide more room to improve and significantly innovate the automated wealth management process and make it more transparent.