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Predicting claims’ reserve is a critical challenge for insurers and has dramatic consequences on their managerial, financial and underwriting decisions. The insurers’ capital and their underwriting capacity of further business are impacted by inaccurate reserve estimates. Increasing premium rates and adjusting the underwriting policy decisions may balance the impact of unexpected claims, but will have a negative impact on their business opportunities. To address this, several papers focusing on the prediction of insurance reserve have been published in the literature. In this paper, we provide a comprehensive review of the research on the insurance reserve prediction techniques in economics and actuarial science literature as well as machine learning and computer science literature. Moreover, we classify these techniques into different approaches based on the prediction mechanism they use in estimation. For each approach, we survey reserve prediction methods, and then show the similarities and differences among them. In addition, the review is armed with a discussion on the challenges and the future opportunities.

Purpose The development of managerial skills is an important priority for small and medium sized firm globally yet we have few insights about the predictors and types of management development (MD) activities in SMEs. To date studies of MD have not sufficiently differentiated between small and medium sized firms. In this paper we investigate the impact of three sets of predictors (contextual, technology and innovation activities, behavioural and skill) on six dimensions of MD (formal internal development, formal internal with an external expert, formal external development, one-to-one MD activities, budget for MD and experiential focused MD). Design/methodology/approach Survey of 360 SMEs in Ireland involving 401 manager and owner managers in small and medium sized firms. Findings Our findings reveal that firm size is an important predictor of the six dimensions of MD investigated in this study. We also found that in terms of the different categories of predictors dimensions of the SME technological and innovation capacity explained differences between small and medium sized firm such as technologically improved product/service, changes existing products and services and process innovation. We also found the age of the firm, the existence of a clearly articulated business strategy and formal strategic planning approaches were significant. Research / practical / policy implications Overall our findings highlight significant differences between small and medium firms which have important research and policy implications. Management development is a government priority for supporting Irish SMEs. We address a fundamental problem providing insight into predictors of management development activities. Originality/value This is a large survey of SMEs in the Republic of Ireland. The findings have important theoretical and policy implications.

The vision set out for this project is to support and enable a fresh approach to growth for SMEs in Ireland where management development is a key strategic priority for SMEs to drive productivity, sustainability, competitiveness and growth, and prepare for the future of work. This research, carried out between October 2019 and March 2020, identifies 7 key findings outlined in the report.

Hospital planning teams are always concerned with optimizing staffing and scheduling decisions in order to improve hospital performance, patient experience, and staff satisfaction. A multi-method approach including data analytics, modeling and simulation, machine learning, and optimization is proposed to provide a framework for smart and applicable solutions for staffing and shift scheduling. Factors regarding patients, staff, and hospitals are considered in the decision. This framework is piloted using the Emergency Department(ED) of a leading university hospital in Dublin. The optimized base staffing patterns and shift schedules actively contributed to solving ED overcrowding problem and reduced the average waiting time for patients by 43% compared to the current waiting time of discharged patients. The reduction was achieved by optimizing the staffing level and then determining the shift schedule that minimized the understaffing and overstaffing of the personnel need to meet patient demand.

High demand of healthcare services due to changes in population demography, technological and medical advancements, budget limitations have direct effect on medical staff and medical organizations in particularly hospitals. One of the major issues confronting the healthcare system is staff behavior when they get close to 'burnout' level. This study identifies factors affecting nurses' behavior and its impact on patients experience time using system dynamics. A particular focus is given to nurses in one of the medical clinical units in one of the largest hospitals in Ireland. Armed with a comprehensive system dynamic model that revolves around the staff stresses, an examination of Skill-Mix, Work Intensity, and Time Per Activity is conducted to examine performance issues due to nurses' fatigue and burnout. Results demonstrate serious consequences on patients' experience time and service quality measures as a proportional result of the increased pressures on nurses in this unit.

Health care services both globally and domestically - are encountering critical issues due to the increasing demand for services at the time of economic recession. Hospital performance is subject to many constraints, and planning is made more difficult by the complexity and uncertainty of demand. Population ageing is creating immense pressures on healthcare facilities across the world, leaving them struggling to cope with the growing demand for elderly healthcare services. Current demand-supply gaps result in prolonged waiting times for patients and substantial cost burdens for healthcare systems due to delayed discharges. This paper reports on a project that uses modelling and simulation to address elderly care pathways in the Irish healthcare sector. The faster management of frail patients admitted to acute hospitals and the introduction of new intermediate care beds are alternative interventions that healthcare executives are interested in simulating to examine their impact on the performance of the elderly care system. The simulation model developed, along with the statistical analysis, enabled hospital services managers to assess the critical performance and financial issues of the current system, and highlighted the decision variables that could significantly improve the flow of elderly patients.