Insight into intraday trading activities including trades, quotes, auction imbalances and security status messages
Finding current exchange rates, conversions and information of major world currencies for foreign currency trading
Finding current exchange rates, conversions and information of major cryptocurrencies for digital currency trading
Automation, optimization, prediction and categorization of high risk and return stocks using AI algorithms
Summarizing large structured and unstructured datasets for easy visualization and data classification
Easy and comfortable to understand graphical features and functions immediately after opening a board
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Over the past few years, financial sector has found values of algorithmic engines that can be brought to many aspects of businesses well beyond the traditional roles of providing a standard set of financial analysis year after year. Now, more and more investors and brokers are looking to access smarter tools to understand complexities of financial markets around the globe. By combining internal financial information and operational data with external information, intelligent stock analytics can address critical business questions with unprecedented ease, speed and accuracy, such as:
AI technology could allow finance companies to establish new business models, reduce investment risks, minimize expenses and maximize returns. It could also enable firms to offer financial services at a level of sophistication, customization and scale never previously possible.
AI enables software to exhibit intelligence, including learning, planning, reasoning, problem solving and decision-making. While the field has advanced in fits and starts since, numerous basic artificial intelligence tools exist today, including virtual assistants Siri (Apple) and Cortana (Microsoft), Google Translate and Netflix’ viewing recommendation engine.
1) Provide forward-looking strategic insights, not just backward-looking financial reporting; 2) Filter and analyze large amounts of data promptly and easily; 3) Combine internal and external data to generate insights that weren’t possible or practical before; 4) Present data and results visually so they are easier to understand and have more impact; 5) Become a data-driven organization that makes investments and operating decisions with more confidence and mitigated risk; and 6) Boost the Finance function’s value and credibility as a strategic partner to the business.
The field of AI has developed symbiotically with the explosion of big data. The explosion of data has coincided with a decline in the cost of collecting and processing digital information, as well as a substantial rise in computing power. This in turn has helped data mining become more affordable resulting in the fast expansion of the big data industry, which is expected to grow to $84.69 billion by 2026 up from $7.6 billion in 2011.
A common view shared by many analysts in the industry is that a sophisticated trading machine capable of learning and thinking will make even today’s most advanced and complex investment algorithms look primitive. AI software is already allowing companies to evaluate deals, investments and strategy in a fraction of the time it takes today’s quantitative analysts, or quants, who build complex—but also somewhat static—models in Excel. Quants can produce several effective models per week. AI machines can construct thousands. AI is a very promising area and can help you find patterns a human would never see. That can give you a huge edge.
AI is facilitating the rise of robots in finance. For example, the Bank of Tokyo-Mitsubishi UFJ introduced its first humanoid robot, NAO, in Tokyo in 2014. The 58-centimer (23-inch) machine can speak nearly 20 languages and read human emotions. The customer service robot welcomes bank clients into the branch, "Hello and welcome. I can tell you about money exchange, ATMs, opening a bank account, or overseas remittance. Which one would you like?" NAO analyzes behavior and facial cues to deliver situation-appropriate responses to client queries. When necessary, NAO, which can recall details from over five million clients and more than 100 financial products, is able to direct individuals to the appropriate bank employee based on the interaction and goals of the customer. Similarly, Mizuho Bank introduced its own customer-facing robot, Pepper, the same year to perform comparable tasks. Pepper, approximately twice the size of NAO, continually evolves and improves its abilities by connecting to thousands of other Peppers through the cloud and interacting with clients.
Expert systems and AI software are allowing companies to evaluate deals, investments and strategy in a fraction of the time it takes today’s quantitative analysts, analysts, or quants, who build complex Excel models.
Investors can fully rely on expert systems and AI engines to acquire more detailed market data and economic information to classify information and perform analysis resulting in superior investment decisions.
AI and machine learning tools are being used to identify new signals on price movements and to make more effective use of the vast amount of available data and market research than with current models.
Considering the inherent nonlinearities of financial data, AI is a promising area in extracting dynamic characteristic of the data and adopting such information into the effective predicting models.
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