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.