Risk Accounting

	Risk Accounting Risk accounting is a method that quantifies granular exposures to non-financial risks, aggregates them, and accounts for these exposures through expected loss accounting provisions. Background Risk accounting is an extension of management accounting, aiming to enhance corporate reporting by measuring and documenting the potential future financial effects of various non-financial risks. These include cyber, supply chain, operational, environmental, geopolitical, conduct, fraud, model, and other types of risks. Current accounting standards acknowledge that a business may face significant non-financial risks in one period, with the financial impacts of these risks reported in subsequent periods. This practice of recognizing risks and potential profits in one period, followed by reporting financial losses in later periods, can undermine stakeholders' trust in reported accounting profits. Moreover, these standards might allow some businesses and individuals to inadequately address ris ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Financial Risk Financial risk is any of various types of risk associated with financing, including financial transactions that include company loans in risk of default. Often it is understood to include only downside risk, meaning the potential for financial loss and uncertainty about its extent. Modern portfolio theory initiated by Harry Markowitz in 1952 under his thesis titled "Portfolio Selection" is the discipline and study which pertains to managing market and financial risk. In modern portfolio theory, the variance (or standard deviation In statistics, the standard deviation is a measure of the amount of variation of the values of a variable about its Expected value, mean. A low standard Deviation (statistics), deviation indicates that the values tend to be close to the mean ( ...) of a portfolio is used as the definition of risk. Types According to Bender and Panz (2021), financial risks can be sorted into five different categories. In their study, they apply an algorith ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Semantic Technology The ultimate goal of semantic technology is to help machines understand data. To enable the encoding of semantics with the data, well-known technologies are RDF (Resource Description Framework) and OWL (Web Ontology Language). These technologies formally represent the meaning involved in information. For example, ontology can describe concepts, relationships between things, and categories of things. These embedded semantics with the data offer significant advantages such as reasoning over data and dealing with heterogeneous data sources. Overview In software, semantic technology encodes meanings separately from data and content files, and separately from application code. This enables machines as well as people to understand, share and reason with them at execution time. With semantic technologies, adding, changing and implementing new relationships or interconnecting programs in a different way can be just as simple as changing the external model that these programs share. Wit ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Natural Language Processing Natural language processing (NLP) is a subfield of computer science and especially artificial intelligence. It is primarily concerned with providing computers with the ability to process data encoded in natural language and is thus closely related to information retrieval, knowledge representation and computational linguistics, a subfield of linguistics. Major tasks in natural language processing are speech recognition, text classification, natural-language understanding, natural language understanding, and natural language generation. History Natural language processing has its roots in the 1950s. Already in 1950, Alan Turing published an article titled "Computing Machinery and Intelligence" which proposed what is now called the Turing test as a criterion of intelligence, though at the time that was not articulated as a problem separate from artificial intelligence. The proposed test includes a task that involves the automated interpretation and generation of natural language ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Machine Learning Machine learning (ML) is a field of study in artificial intelligence concerned with the development and study of Computational statistics, statistical algorithms that can learn from data and generalise to unseen data, and thus perform Task (computing), tasks without explicit Machine code, instructions. Within a subdiscipline in machine learning, advances in the field of deep learning have allowed Neural network (machine learning), neural networks, a class of statistical algorithms, to surpass many previous machine learning approaches in performance. ML finds application in many fields, including natural language processing, computer vision, speech recognition, email filtering, agriculture, and medicine. The application of ML to business problems is known as predictive analytics. Statistics and mathematical optimisation (mathematical programming) methods comprise the foundations of machine learning. Data mining is a related field of study, focusing on exploratory data analysi ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
	Risk Analytics Analytics is the systematic computational analysis of data or statistics. It is used for the discovery, interpretation, and communication of meaningful patterns in data, which also falls under and directly relates to the umbrella term, data science. Analytics also entails applying data patterns toward effective decision-making. It can be valuable in areas rich with recorded information; analytics relies on the simultaneous application of statistics, computer programming, and operations research to quantify performance. Organizations may apply analytics to business data to describe, predict, and improve business performance. Specifically, areas within analytics include descriptive analytics, diagnostic analytics, predictive analytics, prescriptive analytics, and cognitive analytics. Analytics may apply to a variety of fields such as marketing, management, finance, online systems, information security, and software services. Since analytics can require extensive computation (see ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]
picture info	Cyber Threat In computer security, a threat is a potential negative action or event enabled by a vulnerability that results in an unwanted impact to a computer system or application. A threat can be either a negative "intentional" event (i.e. hacking: an individual cracker or a criminal organization) or an "accidental" negative event (e.g. the possibility of a computer malfunctioning, or the possibility of a natural disaster event such as an earthquake, a fire, or a tornado) or otherwise a circumstance, capability, action, or event (incident is often used as a blanket term). A ''threat actor'' who is an individual or group that can perform the threat action, such as exploiting a vulnerability to actualise a negative impact. An '' exploit'' is a vulnerability that a threat actor used to cause an incident. Standard definitions A more comprehensive definition, tied to an Information assurance point of view, can be found in "''Federal Information Processing Standards (FIPS) 200, Minimum ... [...More Info...] [...Related Items...] OR: [Wikipedia] [Google] [Baidu]