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) …
While the use of non-traditional, or alternative, data sources is gaining ground, information culled from social media remains a challenge especially for use in making lending decisions. For that reason, the FICO credit rating score remains "extremely powerful," said Spencer Robinson, head of strategy at Kabbage, a startup that uses algorithms based on large data sets to make lending decisions to small businesses. While the use of non-traditional, or alternative, data sources is gaining ground, information culled from social media remains a challenge especially for use in making lending decisions. Like the use of alternative data, fintech's foray into artificial intelligence (AI) and machine learning is an evolution, not revolution.
For fintechs and FIs, alternative data is typically gathered through machine learning and artificial intelligence. The Boston-based startup applies artificial intelligence and biologically-based machine learning techniques to provide lenders with non-linear, dynamic models of credit risk for their customers (both consumer and small businesses). One example is analyzing numbers as a time series: meaning recognizing there's a difference when, for instance, someone misses 3 payments in 3 months and someone misses 3 payments in 18 months. For those companies that do not want to ignore this market, FICO makes little sense, Underwrite.ai's Mike Armstrong, president of Zest Finance (another platform that uses MI to provide data to consumer lenders), referred to the Consumer Financial Protection Bureau issued "no action letter," last week as a feat for the alternative data model.
Although big data is usually directly associated with machine learning, there is still a debate whether new data sources, such as web crawling through news or social media, credit card data, geolocation data, and so on, is helpful in the investment process. The Barclays report states that 54% of surveyed investment managers use alternative data, such as web crawling social media data, satellite data, or credit card data. Despite the widespread use of alternative data, 80% of surveyed investment managers in the Barclays report said that their biggest challenge was in assessing the usefulness of the data. The Barclays report confirms this potential by noting that the most popular use case for machine learning among respondents is to clean traditional data sources, such as tick data, with 88% of those managers who use machine learning in the investment process using it as a data processing tool.
JP Morgan's recently released 280-page report Big Data and AI Strategies – Machine Learning and Alternative Data Approaches to Investing paints a picture of a future in which alpha is generated from data sources like social media, satellite imagery and machine-classified company filings and news releases. Alpha generation has always been about information advantage: having access either to uncommon insights gained through ingenuity, or common insights acted upon before everyone else. JP Morgan's Contract Intelligence System processes the paperwork for financial deals that previously took tens of thousands of human hours annually. Retiring old systems and moving to integration and data-centricity will require investment and some decent amount of vision, but it will result in future opportunities and cost savings: both from automation and from the ability of such systems to better take advantage of rapidly accelerating advancements in AI, which will require smart data collection, processing and management.
Delegates at Newsweek and International Business Times' data science in capital markets event were mesmerised by a video of shoe box-sized satellites, known as "cube sats" being released into the earth's atmosphere. Professor David Hand, chief scientific advisor, Winton, introducing the event, pointed out that the current AI summer is characterised by what he called "automatic data capture". Tammer Kamel, CEO, Quandl, is a data specialist who understands the transient nature of alpha-generating advantages. Peter Hafez, chief date scientist, RavenPack pointed out that they beat Thomson-Reuters by six months to bring out the first sentiment product.
The landscape of data is ever-changing, meaning analysts need to evolve both their thinking and data collection methods to stay ahead of the curve. In many cases, data that might have been considered unique, uncommon or unattainably expensive just a few years ago is now widely used and often very affordable. It is the analysts who take advantage of these untapped data sources, while they remain untapped, who can reap the rewards by gaining a competitive advantage before the rest of their industry or peers catch on. This type of data is often referred to as alternative data, and with the ever-increasing levels of data available in the modern world comes the opportunity to gain unique insights, competitive industry advantage, and boosted profits. It is perhaps no surprise then to hear that the scramble to get hold of such data has been dubbed the new gold rush.