https://www.govconwire.com/2019/12/palantir-to-help-integrate-army-data-under-other-transaction-agreement/ GovCon Wire - “The problem for Army’s leaders and commanders at every level is you’re trying to make decisions off of accurate data but the data is stored in so many places, it’s often very hard to figure out what’s going on,” said Doug Philippone, lead of global defense at Palantir. “What we did was integrate thousands and thousands of different data tables together so that you can make better decisions.”
https://tabbforum.com/opinions/the-problem-with-algo-wheels/ Michael Mollemans - TABB Group "The problem with algo wheels, however, is that they tend not to account for the risk factors underlying the fat-tail distribution of algo returns, and algo wheel selection processes often turn into a game of guessing the finer points in the algo wheel’s logic. The better the guess, the better the fit to the algo wheel, the better the ranking. All in all, algo wheels need to go beyond gross median cost calculations to include standard deviation risk factors, as well as netting out market and sector betas, or the end result likely will be broker algo rankings that just oscillate between best to worst and then from worst to best along a mean reverting system over time." Click here for other articles on the wheel.
http://www.globenewswire.com/news-release/2019/12/17/1961861/0/en/InfoReach-Includes-Algo-Wheel-with-TMS.html InfoReach TMS Algo Wheel includes: Broker and algo selection backed by machine learning Best execution reporting Improved performance Decision making based on Historical performance with a variety of benchmarks Commission schedules and commitment levels Which broker has received orders in the same instrument TMS’s Algo Wheel is an excellent option for many buy-side teams.
https://tabbforum.com/opinions/ai-washing-is-it-machine-learning-or-worse/ Dr. Bimal Roy Bhanu AiXPRT - "In terms of regulatory compliance in financial services – for example, automating the KYC processes for AML and CTF – the utopia is an AI solution system that harnesses machine learning and natural language algorithms. The AI engine should not be static; rather, it should be trainable to understand any regulation regardless of geography. Also, once the system has learned a regulation, it should be simple – using a straightforward text input interface – to teach the engine to understand any differences or changes in regulation. It might take a person weeks to understand and be trained for changing regulations, whereas the AI solution can do it in a matter of hours. Given that it can take months to manually complete compliance assurance processes, the business case for embracing the automated efficiencies, cost savings and analytics delivered by AI compliance solutions is undeniable. Such platforms are available and are infinitely superior to the illusory masquerade of the AI-washing brigade."
https://hbr.org/2019/11/how-machine-learning-pushes-us-to-define-fairness?utm_source=twitter&utm_medium=social&utm_campaign=hbr David Weinberger - As we look at higher levels of abstraction — from using sliders to adjust the mixes in the bins, to questions about optimizing possibly inconsistent values — ML is teaching us that fairness is not simple but complex, and that it is not an absolute but a matter of trade-offs.
https://www.finextra.com/newsarticle/34515/ai-permeates-capital-markets---study Greenwich Associates report - With 44% already using AI and another 17% reporting plans to implement it in trading in the next 12 to 24 months, a solid majority of market participants — including buy-side institutions, sell-side firms, exchanges, and others — soon will be using AI in the securities trading process.
https://martechseries.com/analytics/data-management-platforms/sp-global-market-intelligence-launches-textual-data-analytics-xpressfeed/ S&P Global Market Intelligence announced the launch of Textual Data Analytics (TDA), a sophisticated new data offering which applies Natural Language Processing to generate sentiment scores and behavioral metrics based on company transcripts
https://www.datanami.com/2019/09/17/whats-the-difference-between-ai-ml-deep-learning-and-active-learning/ Kiran Vajapey - Today, the terms artificial intelligence (AI) and machine learning (ML) are often used interchangeably. While the terms are related, they mean different things. We map out how they all relate to one another, so your team can find the best candidates, best approaches and best frameworks as you embark upon your AI journey.
https://tabbforum.com/opinions/buy-side-turning-to-technology-to-meet-todays-challenges/ Monica Summerville - The practical reality of this means robotic process automation (RPA), as opposed to AI – e.g., machine learning (ML) and Natural Language Processing (NLP) techniques – are more likely to be implemented to start.
https://investingnews.com/daily/tech-investing/fintech-investing/factset-joins-forces-with-datarobot/ FactSet (NASDAQ:FDS), financial analytics company announced that it has partnered with DataRobot, an enterprise AI company. DataRobot's services integrate predictive analytic tools into investment workflows, extending from predicting macroeconomic events to applications for bond performance. As quoted in the press release: "Clients are looking for more effective data and AI tools that will help them ...