30 junho 2016
Toffler
Alvin Toffler, autor de algunas de las predicciones más lúcidas sobre el cambio tecnológico de la segunda mitad del siglo XX y la adaptación de las sociedades a la tecnología, murió este lunes en su casa de Los Ángeles a los 87 años. Toffler alcanzó fama mundial en 1970 con el libro El shock del futuro, al que siguió La tercera ola (1980), en los que describía los cambios a los que se enfrentarían los países industrializados, cuyas economías pasarían a ser posindustriales y basadas en el conocimiento, y el impacto de los cambios tecnológicos rápidos en la sociedad. (Fonte: El País)
Lucros contábeis e previsão do PIB
Resumo:
We document that aggregate accounting earnings growth is an incrementally significant leading indicator of growth in nominal Gross Domestic Product (GDP). Professional macro forecasters, however, do not fully incorporate the predictive content embedded in publicly available accounting earnings data. As a result, future nominal GDP growth forecast errors are predictable based on accounting earnings data that are available to professional macro forecasters in real time.
Fonte: Yaniv Konchitchki, Panos N. Patatoukas, Accounting earnings and gross domestic product, Journal of Accounting and Economics, Volume 57, Issue 1, February 2014, Pages 76-88, ISSN 0165-4101, http://dx.doi.org/10.1016/j.jacceco.2013.10.001.(http://www.sciencedirect.com/science/article/pii/S016541011300058X)
We document that aggregate accounting earnings growth is an incrementally significant leading indicator of growth in nominal Gross Domestic Product (GDP). Professional macro forecasters, however, do not fully incorporate the predictive content embedded in publicly available accounting earnings data. As a result, future nominal GDP growth forecast errors are predictable based on accounting earnings data that are available to professional macro forecasters in real time.
Fonte: Yaniv Konchitchki, Panos N. Patatoukas, Accounting earnings and gross domestic product, Journal of Accounting and Economics, Volume 57, Issue 1, February 2014, Pages 76-88, ISSN 0165-4101, http://dx.doi.org/10.1016/j.jacceco.2013.10.001.(http://www.sciencedirect.com/science/article/pii/S016541011300058X)
29 junho 2016
Alienados
Os estadunidenses são considerados alienados. Não se preocupam com o que ocorre no mundo. Mas quais os países que mais despertam interesse dos gringos? Usando a ferramenta de pesquisa do Google é possível saber o grau de interesse deles. O primeiro colocado é uma surpresa, pois não se trata do parceiro histórico e origem do país, o Reino Unido, mas sim o polêmico, para eles, México. O segundo colocado na lista de pesquisa é mais surpreendente ainda: a Índia, que desperta mais interesse que o Canadá (terceiro), Japão (quinto) ou China (sexto). É também uma surpresa a Indonésia, em sétimo.
Brasil? Em 13º, na frente de França e Espanha. Eis a lista:
1 – México
2 – Índia
3 – Canadá
4 – Reino Unido
5 - Japão
6 - China
7 - Indonésia
8 - Itália
9 - Austrália
10 – Rússia
12 - Alemanha
13 – Brasil
14 – França
15 – Espanha
16 - Turquia
Brasil? Em 13º, na frente de França e Espanha. Eis a lista:
1 – México
2 – Índia
3 – Canadá
4 – Reino Unido
5 - Japão
6 - China
7 - Indonésia
8 - Itália
9 - Austrália
10 – Rússia
12 - Alemanha
13 – Brasil
14 – França
15 – Espanha
16 - Turquia
Inteligência Artificial pode melhorar a qualidade da Auditoria
[...]
One of the hottest areas of innovation today is the field of artificial intelligence (AI). AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Because AI technologies—also called cognitive technologies—extend the power of information technology to tasks traditionally performed by humans, they enable users to break prevailing tradeoffs between speed, cost, and quality.
Such technologies can enable auditors to automate tasks that have been conducted manually for decades, such as counting inventories or processing confirmation responses. And as a result, auditors can be liberated to focus on enhancing quality by evaluating advanced analytics, spending more time exercising their professional judgment, and providing greater insights.
One specific area in which auditors are taking advantage of the benefits of cognitive technologies is document review. Reading through stacks of contracts to extract key terms has traditionally been a time-consuming, manual process. Cognitive technologies are already being deployed by forward-thinking firms to largely automate this process. Natural language processing (NLP) technology reads and understands key concepts in the documents. And machine-learning technology makes it possible to train the system on a set of sample contracts so that it learns how to identify and extract key terms.
Contract review powered by cognitive technologies can now take a fraction of the time it used to. AI allows an auditor to review and assess larger samples—even up to 100% of the documents. And it makes it possible to do lightning-fast analytics—automating the separation of documents that contain escalation clauses from those that don’t, for instance, and visualizing the degree of variability from a standard form across a population of documents. This contributes to enhanced quality and delivery of insights to audit committees faster.
And this is just the beginning. We’re using information technology to accelerate and enhance many other parts of the audit process as well. And we see many opportunities to innovate and advance the profession with cognitive technologies to take audit quality and insight to the next level.
For instance, we are using workflow automation technology to streamline the confirmations process by providing stakeholders with a single digital environment to prepare, authorize, distribute, collect, manage, and evaluate the results of the confirmations process.
In the future, machine learning could be used to recognize, extract, and process values from the many supporting documents typically attached to a confirmation, automatically confirming the transaction without significant auditor intervention. And NLP could enable the system to handle anomalies and exceptions automatically by reading and understanding free-form textual responses from counterparties and recommending appropriate action.
Using cognitive technologies, auditors may soon be able to provide clients with new ways to uncover risk hiding in plain sight in financial statements. Today, our professionals use tools that parse financial statements automatically, making it easy to find footnotes and conduct thorough peer comparisons.
In the future, machine learning and natural language processing could make it possible to scan financial statements and suggest risks associated with the text while linking disclosures to Securities and Exchange Commission comment letters, analyst reports, and social media sentiment.
Researchers have already shown that it’s possible to predict the volatility of a company’s stock price by applying automated analysis, powered by natural language processing and machine learning, to a company’s financial statements. It’s not hard to imagine generalizing this approach to surface other types of risk.
Finally, consider how AI might change the inventory count process, a task that is typically as old-fashioned as it gets: visiting a client and taking inventory of materials and finished goods, clipboard in hand. Today, we are equipping staff with tablet and smartphone applications that collect and automatically consolidate inventory count results to the group auditor in real-time.
I can imagine turbocharging the inventory process by using smartphone cameras and computer vision to automatically identify and count items, spot patterns, and flag anomalies. This kind of application already exists in health care, where visiting nurses can snap a photo of a home-bound patient’s medications, automatically identifying them and ensuring the patient has what she needs.
Using cognitive technologies to evolve the audit process by making it smarter, more insightful, and more efficient is just one of the ways the audit profession is innovating. This isn’t self-driving cars. It is the future of the audit profession. And the users of financial statements deserve it.
Fonte: Jon Raphael is chief innovation officer at Deloitte & Touche LLP.
One of the hottest areas of innovation today is the field of artificial intelligence (AI). AI is the theory and development of computer systems able to perform tasks that normally require human intelligence. Because AI technologies—also called cognitive technologies—extend the power of information technology to tasks traditionally performed by humans, they enable users to break prevailing tradeoffs between speed, cost, and quality.
Such technologies can enable auditors to automate tasks that have been conducted manually for decades, such as counting inventories or processing confirmation responses. And as a result, auditors can be liberated to focus on enhancing quality by evaluating advanced analytics, spending more time exercising their professional judgment, and providing greater insights.
One specific area in which auditors are taking advantage of the benefits of cognitive technologies is document review. Reading through stacks of contracts to extract key terms has traditionally been a time-consuming, manual process. Cognitive technologies are already being deployed by forward-thinking firms to largely automate this process. Natural language processing (NLP) technology reads and understands key concepts in the documents. And machine-learning technology makes it possible to train the system on a set of sample contracts so that it learns how to identify and extract key terms.
Contract review powered by cognitive technologies can now take a fraction of the time it used to. AI allows an auditor to review and assess larger samples—even up to 100% of the documents. And it makes it possible to do lightning-fast analytics—automating the separation of documents that contain escalation clauses from those that don’t, for instance, and visualizing the degree of variability from a standard form across a population of documents. This contributes to enhanced quality and delivery of insights to audit committees faster.
And this is just the beginning. We’re using information technology to accelerate and enhance many other parts of the audit process as well. And we see many opportunities to innovate and advance the profession with cognitive technologies to take audit quality and insight to the next level.
For instance, we are using workflow automation technology to streamline the confirmations process by providing stakeholders with a single digital environment to prepare, authorize, distribute, collect, manage, and evaluate the results of the confirmations process.
In the future, machine learning could be used to recognize, extract, and process values from the many supporting documents typically attached to a confirmation, automatically confirming the transaction without significant auditor intervention. And NLP could enable the system to handle anomalies and exceptions automatically by reading and understanding free-form textual responses from counterparties and recommending appropriate action.
Using cognitive technologies, auditors may soon be able to provide clients with new ways to uncover risk hiding in plain sight in financial statements. Today, our professionals use tools that parse financial statements automatically, making it easy to find footnotes and conduct thorough peer comparisons.
In the future, machine learning and natural language processing could make it possible to scan financial statements and suggest risks associated with the text while linking disclosures to Securities and Exchange Commission comment letters, analyst reports, and social media sentiment.
Researchers have already shown that it’s possible to predict the volatility of a company’s stock price by applying automated analysis, powered by natural language processing and machine learning, to a company’s financial statements. It’s not hard to imagine generalizing this approach to surface other types of risk.
Finally, consider how AI might change the inventory count process, a task that is typically as old-fashioned as it gets: visiting a client and taking inventory of materials and finished goods, clipboard in hand. Today, we are equipping staff with tablet and smartphone applications that collect and automatically consolidate inventory count results to the group auditor in real-time.
I can imagine turbocharging the inventory process by using smartphone cameras and computer vision to automatically identify and count items, spot patterns, and flag anomalies. This kind of application already exists in health care, where visiting nurses can snap a photo of a home-bound patient’s medications, automatically identifying them and ensuring the patient has what she needs.
Using cognitive technologies to evolve the audit process by making it smarter, more insightful, and more efficient is just one of the ways the audit profession is innovating. This isn’t self-driving cars. It is the future of the audit profession. And the users of financial statements deserve it.
Fonte: Jon Raphael is chief innovation officer at Deloitte & Touche LLP.
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