Concirrus Quest Marine's Insurance Business Model: The Role of AI and Big Data

2021 ◽  
Author(s):  
Paul D. Timms ◽  
Christopher P. Holland ◽  
Andrew Yeoman
2014 ◽  
pp. 79-130 ◽  
Author(s):  
Ales Novak

The term ?business model' has recently attracted increased attention in the context of financial reporting and was formally introduced into the IFRS literature when IFRS 9 Financial Instruments was published in November 2009. However, IFRS 9 did not fully define the term ‘business model'. Furthermore, the literature on business models is quite diverse. It has been conducted in largely isolated fashion; therefore, no generally accepted definition of ?business model' has emerged. Therefore, a better understanding of the notion itself should be developed before further investigating its potential role within financial reporting. The aim of this paper is to highlight some of the perceived key themes and to identify other bases for grouping/organizing the literature based on business models. The contributions this paper makes to the literature are twofold: first, it complements previous review papers on business models; second, it contains a clear position on the distinction between the notions of the business model and strategy, which many authors identify as a key element in better explaining and communicating the notion of the business model. In this author's opinion, the term ‘strategy' is a dynamic and forward-looking notion, a sort of directional roadmap for future courses of action, whereas, ‘business model' is a more static notion, reflecting the conceptualisation of the company's underlying core business logic. The conclusion contains the author's thoughts on the role of the business model in financial reporting.


Urban Studies ◽  
2021 ◽  
pp. 004209802110140
Author(s):  
Sarah Barns

This commentary interrogates what it means for routine urban behaviours to now be replicating themselves computationally. The emergence of autonomous or artificial intelligence points to the powerful role of big data in the city, as increasingly powerful computational models are now capable of replicating and reproducing existing spatial patterns and activities. I discuss these emergent urban systems of learned or trained intelligence as being at once radical and routine. Just as the material and behavioural conditions that give rise to urban big data demand attention, so do the generative design principles of data-driven models of urban behaviour, as they are increasingly put to use in the production of replicable, autonomous urban futures.


2021 ◽  
Vol 13 ◽  
pp. 175628722199813
Author(s):  
B. M. Zeeshan Hameed ◽  
Aiswarya V. L. S. Dhavileswarapu ◽  
Nithesh Naik ◽  
Hadis Karimi ◽  
Padmaraj Hegde ◽  
...  

Artificial intelligence (AI) has a proven record of application in the field of medicine and is used in various urological conditions such as oncology, urolithiasis, paediatric urology, urogynaecology, infertility and reconstruction. Data is the driving force of AI and the past decades have undoubtedly witnessed an upsurge in healthcare data. Urology is a specialty that has always been at the forefront of innovation and research and has rapidly embraced technologies to improve patient outcomes and experience. Advancements made in Big Data Analytics raised the expectations about the future of urology. This review aims to investigate the role of big data and its blend with AI for trends and use in urology. We explore the different sources of big data in urology and explicate their current and future applications. A positive trend has been exhibited by the advent and implementation of AI in urology with data available from several databases. The extensive use of big data for the diagnosis and treatment of urological disorders is still in its early stage and under validation. In future however, big data will no doubt play a major role in the management of urological conditions.


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