It is perhaps easier to explain by first touching on RegTech or Regulatory Technology, which is the application of technology to enhance the way regulatory compliance is done in financial institutions. As an example, a potential application of RegTech is in the area of anti-money laundering and countering financing of terrorism (AML/CFT for short) where high risk AML/CFT cases can be used to train a machine learning algorithm to identify potential future cases that financial institutions can investigate further. It will seek inputs from the financial industry to see how best we can enhance and streamline MAS' data collection and management process, and put in place the necessary infrastructure and platforms to achieve this goal. Finally, we want to walk the talk, lead by example and put all the key elements of data, tools, infrastructure and skillsets in place within MAS to drive a data culture.
For example, if we talk about the healthcare sector, Sophia Genetics is a data-driven medical company which has developed SOPHiA for genomics testing. Especially in a country like Africa, it has given hospitals an opportunity to conduct genomic testing at the same level as any other hospital in a developed country. I would recommend reading a very interesting study done by Accenture and Frontier research on the future of AI - Why Artificial Intelligence is the Future of Growth. The study claims that if AI has been made a part of the global economies by the year 2035, countries would enjoy tremendous growth rates.
Regulators require financial institutions to estimate counterparty default risks from liquid CDS quotes for the valuation and risk management of OTC derivatives. However, the vast majority of counterparties do not have liquid CDS quotes and need proxy CDS rates. Existing methods cannot account for counterparty-specific default risks; we propose to construct proxy CDS rates by associating to illiquid counterparty liquid CDS Proxy based on Machine Learning Techniques. This paper is, to the best of our knowledge, the first systematic study of CDS Proxy construction by Machine Learning techniques, and the first systematic classifier comparison study based entirely on financial market data.
Applications of AI and machine learning are exploding around the world as businesses and governments buy into the fast-developing technology to cut costs, improve security and better serve their customers and citizens. Research by Accenture, a leader in artificial intelligence research and development, sees economic growth rates of many developed countries doubling by 2035 due to AI, with labor productivity increasing as much as 40 percent. The survey identifies AI followers as marketing companies, which primarily use AI to gauge future customer purchases, improve media buying, monitor social media and tailor promotions. Maria Marinina on CustomerThink.com recommends trying Quill Engage to quickly produce useful reports based on Google Analytics data.
A new McKinsey Global Institute report finds realizing automation's full potential requires people and technology to work hand in hand. As processes are transformed by the automation of individual activities, people will perform activities that complement the work that machines do, and vice versa. Factors that will determine the pace and extent of automation include the ongoing development of technological capabilities, the cost of technology, competition with labor including skills and supply and demand dynamics, performance benefits including and beyond labor cost savings, and social and regulatory acceptance. While much of the current debate about automation has focused on the potential for mass unemployment, people will need to continue working alongside machines to produce the growth in per capita GDP to which countries around the world aspire.
A recent study by Redwood Software and the Centre for Economic and Business Research (Cebr) has focused on the impact of robotics automation on economic development across OECD countries, including the UK and the US. How will Brexit impact the UK robotics industry? "Given the tough political climate, there are certainly interesting times ahead for the robotics market in the UK," said David Whitaker, Managing Economist at Cebr. "Growth and modernisation in the automotive industry has been a key driver of UK robotics growth in recent years.
Innovations in digitization, analytics, artificial intelligence, and automation are creating performance and productivity opportunities for business and the economy, even as they reshape employment and the future of work. Rapid technological advances in digitization and data and analytics have been reshaping the business landscape, supercharging performance, and enabling the emergence of new business innovations and new forms of competition. At the same time, the technology itself continues to evolve, bringing new waves of advances in robotics, analytics, and artificial intelligence (AI), and especially machine learning. Some companies are gaining a competitive edge with their use of data and analytics, which can enable faster and larger-scale evidence-based decision making, insight generation, and process optimization.
However, the advent of Artificial Intelligence (AI) will impact the world economy in a way that no technology in the past was able to. Ford quotes from Hayek's book Law, Legislation and Liberty: "The assurance of a certain minimum income for everyone, or a sort of floor for himself, appears not only to be a wholly legitimate protection against a risk common to all, but a necessary part of the Great Society in which the individual no longer has specific claims on the members of the particular small group into which he was born". For example: "...even without accounting for likely future improvements in their designs, machine learning systems powered by deep learning networks are virtually certain to see continued dramatic progress simply as a result of Moore's Law [pg 95 ]" and "once AGI is achieved, Moore's Law alone would likely soon produce a computer that exceeded human intellectual capability [pg 228 ]." The argument goes like this: In general, outsourcing is part of free trade and comparative advantage.
I forecast; Turkish Lira / USD Exchange Rates (Period: Daily, Range: 2002-2005 for Training, 2005-2006 for forecasting) I programmed this network on Visual Basic.Net and I'll tray to give an abstract of my results in here. I used many of types of ANNs and I decided to best way of the forecasting of time series are Feedforward Backpropagation Networks. And yes there are absolutely significant differences between ANN and other techniques espicially "When the relations of series are both not linear and unseenable easily" In Accordance With: Mean Error Criteria In Accordance With: Mean Absolute Error Criteria In Accordance With: Mean Squared Error Criteria In Accordance With: Mean Percentage Error Criteria In Accordance With: Mean Absolute Percentage Error Criteria I have given the most useful criterias that uses to performance analyzing of forecast. Model: USD f(USD(t-1), XU100(t-1), Gold(t-1), FED_Euro(t-1)) USD: USD/TL Exchange Rate XU_100: Istanbul Stock Exchange 100 Index Gold: Istanbul Gold Market TL Price of Gold Fed_Euro: Federal Reserve Bank of USA EURO/USD exchange rate Activation Function: Hyperbolic Tangent Normalization Range of Data: (0,1) (continuous) Learning Rate: 0.01 Lambda for Adaptive Learning: 0.0001 Beta for Adaptive Learning: 0.02 Momentum Term: 0.90 REG MODEL: Multiple Regression Model with same regressors (but used to optimal lag specification) GARCH MODEL: Generalized Autoregressive Conditional Heteroscedasticity Model with same regressors (but used to optimal lag specification) VAR MODEL: Vector Autoregressive Model with same regressors (but used to optimal lag specification) PS: All the time series are growth series of rates, so those were found stationary at constant level as per Augmented Dickey Fuller and DF(82) tests.