Last year, most (if not all) financial technology providers either completed or started major projects that involved moving their products and services to the cloud. WatersTechnology looks at 15 of the more interesting cloud-migration initiatives from…
Buy-side firms are using patent and other types of data to identify trailblazer companies from phonies.
WatersTechnology looks at how 10 different firms are embedding machine learning algorithms into their platforms and tools.
WatersTechnology looks at some of the major projects coming out of Asia that are leading the way for firms around the globe.
This year, natural language processing came to the fore in capital markets, helping firms of all kinds parse huge, unstructured datasets.
The firm is working with different machine-learning methods for portfolio construction, and expects its AI system to go live early next year.
Anthony looks at how two major tech companies in the capital markets space are evolving their cloud strategies and what they might mean for the industry at large.
The company aims to show that pairing good quality data with knowledge graphs can lead to links that previously would have been missed.
The trading platform is working to develop its pre-trade automation capabilities to predict a bond’s likelihood of execution, and helping buy-side clients navigate fixed income trading protocols.
Technologists are working to automate indications of interest from trading desks, according to UBS’s head of machine learning.
Regardless of fund type, portfolio managers can analyze their trade data and behavior to make improvements, according to Essentia Analytics’ CEO.
This Week: Janus/SS&C; Wolters Kluwer; Tora/Neptune; Standard Chartered/Northern Trust; SimCorp; & More
A summary of some of the past week's financial technology news.
Agent-based modeling has taken root seemingly everywhere throughout the last decade, from theoretical physics, to military operations, to public health, to ride-sharing apps like Uber, and to a much lesser extent, finance. However, a year such as 2020…
Anthony provides some of his initial questions and thoughts following the S&P-IHS Markit deal. He also takes a second look at AML technology after getting some sage feedback.
JP Morgan Chase's head of AI technology and Deutsche Bank's head of innovation for the Americas at Deutsche Bank join to talk about AI, cloud, and more.
Sell-side firms and data providers are increasingly experimenting with natural-language generation to create new forms of automatically curated reports, emails and alerts, but the technique comes with significant challenges.
The data provider is using natural language generation to summarize news articles and write automated stories.
The QED team within the asset manager is aligning its focus next year to deliver a holistic AI platform to its investment professionals—including a recommendation engine.
Anthony says that while machine-learning models have been hit-and-miss during the pandemic, NLP is taking on greater importance. He also looks at how exchanges are looking to move their core matching engines to the cloud.
Quants wrestle with how far into the past their machine learning models should peer.
The supporters of a plan for a federated cloud architecture in Europe held a conference to discuss development plans, but it’s still unclear how the concept will work in practice.
UBS AM’s Bryan Cross says the goal of embedding AI in the investment process has failed because the aim has been misguided.
Although graph technology is still in the early stages of adoption, banks such as Wells Fargo and ING have begun leveraging it to find previously unknown connections between datasets.
Wei-Shen thinks about how the battle for supremacy in AI will evolve in the near future, and what the implications of China’s advances in the field might mean for Wall Street technology.