alternative data


More Jobs, Jobs, Jobs Thanks to AI - DiscoveryVest

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In that DiscoveryVest provides data recognition services, we fully appreciate that within the last couple of years, computers have become better at object recognition than humans. We also know that there are many misnomers out there about AI and the effect on human workers. Here's the truth. More AI...


Need a job? Why artificial intelligence will help human workers, not hurt them

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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.


New Frontier of Artificial Intelligence in Banking

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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. Artificial Intelligence is poised to revolutionize payments for many of the same reasons it has become so important in other areas of the financial industry. In order to stay current, it's paramount that businesses begin to understand the capabilities of AI. Artificial intelligence applications can conquer the challenge of rewarding loyal customers for their business in ways that are directly relevant to those individuals.


Three trends alternative asset managers must watch out for in 2018

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Digitalisation, alternative data sets and enterprise data analytics will be critical in decision making for asset managers seeking high returns in the coming year, according to an outlook for the alternative asset management sector by Indus Valley Partners. Indus Valley, a provider of technology solutions for alternative asset managers, said in a statement that traditional and alternative asset managers will face a number of hurdles in 2018. With pressure growing from investors and regulators, many asset managers will seek new ways to earn high returns through quantitative strategies, alternative data sets and machine learning, it said. Artificial Intelligence and machine learning will become critical in the search for high returns. Blockchain technology will be used in pilot programs to achieve post-trade operational efficiency and for better regulatory compliance, according to the outlook.


How Much Can We Trust AI?

International Business Times

Artificial intelligence software gets more sophisticated almost every day. Computers can already beat human experts at chess, stock market predictions, and detecting cancer. Yet most machine learning experts believe we shouldn't hand over complete control to AI-powered robots any time soon. "We don't trust autonomous vehicles yet, despite the fact that they rarely make mistakes, because the cost of error is too high," New York University professor Vasant Dhar, who also founded one of the longest running AI-powered hedge funds, told International Business Times. "It takes a while to learn the machine learning program's style.


The Next Phase for Fintech: Moving Beyond a Buzzword

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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.


The Rise of Alternative Data in the Lending Market

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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.


Behind the hype: Machine learning in investment management

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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.


FOMO And The Adoption Of AI In Finance

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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.


What Is Automatic Data Capture? How Hedge Funds Can Trade On Heaven-Sent Data

International Business Times

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.