Influence of AI and Machine Learning in Insurance Sector

The Aim of this research is to identify influence, usage, and the benefits of AI (Artificial Intelligence) and ML (Machine learning) using big data analytics in Insurance sector. Insurance sector is the most volatile industry since multiple natural influences like Brexit, pandemic, covid 19, Climate changes, Volcano interruptions. This research paper will be used to explore potential scope and use cases for AI, ML and Big data processing in Insurance sector for Automate claim processing, fraud prevention, predictive analytics, and trend analysis towards possible cause for business losses or benefits. Empirical quantitative research method is used to verify the model with the sample of UK insurance sector analysis. This research will conclude some practical insights for Insurance companies using AI, ML, Big data processing and Cloud computing for the better client satisfaction, predictive analysis, and trending.

Analysis of Urban Agglomeration in Economic and Legal Perspectives (A Study on the Development of Industrial Agglomeration Areas in Semarang City)

<em>This study tries to analyze the implementation of laws and policies regarding the development of urban agglomerations, analyze agrarian law regulations in Indonesia addressing industrial estate issues, and analyze the leading sectors and economic potential of the city of Semarang. In this study, the authors use the Base Sector Analysis/LQ Analysis, Economic Sector Performance Analysis/Shift Share Analysis, Growth Ratio Model Analysis/GRM, Klassen Typology Analysis, Overlay Techniques, and SWOT Analysis to measure the competitive advantage of each element), and provide policy recommendations for the government as well as for industry players. The object of this research is spread over several industrial areas located in Semarang City including Wijaya Kusuma Industrial Area, Candi Industrial Area, BSB Industrial Park, and Terboyo Industrial Estate. The findings of this study are that it is known that leading sectors such as warehousing, financial services, transportation, retail, real estate, trade, and construction are economic bases that are growing rapidly and can support the development of urban agglomerations.</em>

Innovation Orientation in the Non-profit Sector: Analysis of Its Impact on the Performance of Spanish Non-governmental Development Organizations

Since 2015, the approval of the 2030 Agenda and of the 17 Sustainable Development Goals (SDGs) has led to a notable reshaping and expansion of the architecture of the international cooperation system. The SDGs mark a new path for the planning processes of the different actors working for development, expanding their goals, proposing an update of the roles they must play, and defining new frameworks for relationships and spaces for action. Non-governmental development organizations (NGDOs), whose traditional mission focused on reducing the poverty gap, defending human rights, or promoting environmental protection, must be able to respond satisfactorily to these new challenges, pass beyond their classic positions, and adapt to an increasingly complex and turbulent global context. Achieving high impact in the SDGs requires development organizations to be more agile and innovative. With the intention of bringing visibility to the importance that innovation can play in the success and results achieved by development cooperation organizations, the main objective of this study was to validate, through a sample of Spanish NGDOs, a causal model that represents the positive contribution which innovation orientation generates in the result of the activity of these entities. This research concludes by confirming that innovation orientation favors the attainment of a higher degree of success in the projects and actions carried out by non-profit entities which promote the SDGs, which has a direct and positive impact on the performance they achieve.

Global policymakers and catastrophic risk

There is a rapidly developing literature on risks that threaten the whole of humanity, or a large part of it. Discussion is increasingly turning to how such risks can be governed. This paper arises from a study of those involved the governance of risks from emerging technologies, examining the perceptions of global catastrophic risk within the relevant global policymaking community. Those who took part were either civil servants working for the UK government, U.S. Congress, the United Nations, and the European Commission, or cognate members of civil society groups and the private sector. Analysis of interviews identified four major themes: Scepticism; Realism; Influence; and Governance outside of Government. These themes provide evidence for the value of conceptualising the governance of global catastrophic risk as a unified challenge. Furthermore, they highlight the range of agents involved in governance of emerging technology and give reason to value reforms carried out sub-nationally.

Sector-by-sector analysis of dependence dynamics between global large-cap companies and infectious diseases: A time-varying copula approach in EBOV and COVID-19 episodes

Infectious diseases and widespread outbreaks influence different sectors of the economy, including the stock market. In this article, we investigate the effect of EBOV and COVID-19 outbreaks on stock market indices. We employ time-varying and constant bivariate copula methods to measure the dependence structure between the infectious disease equity market volatility index (IEMV) and the stock market indices of several sectors. The results show that the financial and communication services sectors have the highest and the lowest negative dependency on IEMV during the Ebola virus (EBOV) pandemic, respectively. However, the health care and energy sectors have the highest and lowest negative dependency on IEMV during the COVID-19 outbreak, respectively. Therefore, the results confirm the heterogeneous time-varying dependency between infectious diseases and the stock market indices. The finding of our study contributes to the ongoing literature on the impact of disease outbreaks, especially the novel coronavirus outbreak on global large-cap companies in the stock market.