If you are looking for an answer to the question What is Artificial Intelligence? and you only have a minute, then here's the definition the Association for the Advancement of Artificial Intelligence offers on its home page: "the scientific understanding of the mechanisms underlying thought and intelligent behavior and their embodiment in machines."
However, if you are fortunate enough to have more than a minute, then please get ready to embark upon an exciting journey exploring AI (but beware, it could last a lifetime) …
Alternative data will likely transform active investment management over the next five years, according to a white paper by Deloitte. Those firms that do not update their investment processes within that timeframe could, they argue, face strategic risks. Alternative data is a wide term that spans multiple categories. In brief, it refers to any non-traditional data (ie market price data, trade volume data) and includes online search data, trade data, satellite and weather data, consumer transaction data, geo-location data, etc. "The amount of data is growing exponentially. IDC said that there were 16.3 zettabytes of information generated in 2017 alone (one zettabyte is 1 billion terrabytes).
Machines might scare policymakers from Brussels to Washington, but artificial intelligence could yield a significant developmental dividend in the developing world. In African markets, the technology behind Alexa and Siri can be harnessed to diagnose illness or address traffic gridlock. Machine learning, whereby algorithms make predictions and improve based on large amounts of data, is often relegated to the realm technologists and the elite; but for the two billion unbanked adults worldwide, this technology could light a path out of poverty by helping traditional lenders approve loans using hundreds of non-traditional data points. AI has the capacity to add value at the individual, small business, and the large corporate level alike across Africa. But today, private data marketplaces such as DataWallet and Meeco allow individuals in the West to sell their personal data directly to buyers.
Credit scoring and approval rates changed substantially with the arrival of alternative lenders, mainly due to the adoption of new practices in collecting and analyzing potential borrower data. Alternative data has played its role in expanding horizons for financial institutions and for creating an opportunity to enter the financial sector fir technology startups and data-rich international companies. While social media, for example, as a source of data for creditworthiness assessment is still at a nascent stage, certain startups are already claiming to have incorporated information from social networks into their frameworks. In the quest to reinvent the way to assess consumer-related risk (as well as extend credit to unscored and questionable), startups were found more imaginative than traditional institutions. Alternative data requires alternative approach to data analytics, which wide adoption of machine learning and artificial intelligence brought.
Bank of America Merrill Lynch hired Rajesh Krishnamachari, formerly a senior quantitative strategist and researcher at J.P. Morgan, as the head of data science for equities in New York last month. BofA's new equities-focused data-science team is using machine learning and artificial intelligence to get insights from proprietary data and develop new products that have an impact on the top and bottom line of the business. A Bank of America spokeswoman confirmed his employment but declined to comment further. Krishnamachari joined J.P. Morgan's equity derivatives quantitative research team in 2014. Primarily using Python, Java and the XGBoost software library, he designed and back-tested systematic options, VIX and equities trading strategies, as well as an ultra-high-frequency execution algorithm for trading VIX futures.
Board to help MOV37 find, develop and nurture the new wave of young talent revolutionizing investment management. New York, March 8, 2018 – MOV37, the research and investment platform for Autonomous Learning Investment Strategies (ALIS), has assembled an Advisory Board to help find, develop and nurture the young talent that will revolutionize investment management. "The Advisory Board's primary role is to push us outside our intellectual comfort zones," says Adil Abdulali, Chief Science Officer and President at MOV37. The Board will partner with MOV37 in exploring how technology is fundamentally changing investment management and identifying and supporting the young ALIS managers at the forefront of that disruption. Raphael Douady earned his math PhD in Hamiltonian systems in Paris and holds the Robert Frey Endowed Chair for Quantitative Finance at Stony Brook, New York.
The value that can be extracted from a growing wealth of data across boundless sectors is only just beginning to be grasped. If you look at search engines or digital commerce platforms, an almost direct relationship exists between the amount of data users willingly give up and the value this has. There is also the fact that those with the most data at their disposal will probably have the best artificial intelligence in the future, making them nigh on invincible. In finance, data of one sort or another has always held intrinsic value. People who trade in the zero-sum game of capital markets all need a Bloomberg terminal or Thomson Reuters data to have a look at all the traditional price information, earnings estimates and so on.
Updated In 2013, James "Jimi" Crawford founded a company called Orbital Insight, barely noticed at the time amid the Silicon Valley froth. Crawford had worked at NASA for 15 years and wrote software for Mars rovers. He left NASA to run engineering for Google Books, and while there he noticed that Elon Musk's SpaceX and other new companies were driving down the cost of building and launching satellites. Orbital Insight's first product looked at images of cornfields all over the world, analyzing the health of plants to predict yields for traders who bet on future price swings.
In 2013, James "Jimi" Crawford founded a company called Orbital Insight, barely noticed at the time amid the Silicon Valley froth. Crawford had worked at NASA for 15 years and wrote software for Mars rovers. He left NASA to run engineering for Google Books, and while there he noticed that Elon Musk's SpaceX and other new companies were driving down the cost of building and launching satellites. Crawford saw an opportunity to collect and analyze what he anticipated would be a deluge of images from a surfeit of new satellites that would circle the Earth, taking readings and pictures. Orbital Insight's first product looked at images of cornfields all over the world, analyzing the health of plants to predict yields for traders who bet on future price swings.
Focused on applications beyond fighting fraud, this is the second article of a two-part series on the deployment of artificial intelligence in banking. Part 1 of Artificial Intelligence – The New Frontier in Banking discussed how important machine learning has become to financial institutions in the fight against fraud.