A deep-dive into how capital markets firms are using open-source tools to experiment with machine learning.
The latest big idea in machine learning is to automate the drudge work in model-building for quants
Quants are embracing the idea of ‘model-free’ pricing and deep hedging.
The hype of artificial intelligence is far from fading—actually, it’s more like a building tidal wave. Wei-Shen wonders if the capital markets will catch the wave, or get smothered by the tide.
With Benoît Legrand firmly in the driver’s seat, the last four years have seen ING push to transform its IT landscape and approach to innovation.
The bank has a number of projects using emerging technologies, one of which optimizes the process of detecting price anomalies.
The bank is looking to pair this relatively new role with its data scientists as a bridge for business professionals.
FIS is pushing to add greater automation for its private-equity business in an effort to streamline workflows.
The regulator already uses machine learning to identify spoofing and layering activities.
BlackRock, MSCI, and La Française are some of the firms looking to replace traditional, linear risk models.
Finance firms and regulators are beginning to assess the ethical implications of artificial intelligence.
The immense growth of online data is driving an increasing number of asset managers to deploy web-scraping tools to find unique investment insights.
Artificially intelligent algorithms are not infallible—as Jo Wright explains, it’s quite the opposite.
Asset managers wanting to thrive in today’s landscape of squeezed margins must learn how to capitalize on innovative AI tech to deliver top-line growth, according to SS&C Technologies.
The call of artificial intelligence and machine learning is alluring. However, Wei-Shen says they can be tough to deal with, especially when shooting at invisible targets.
The Japanese bank has already automated handwritten form processing and is experimenting with AI to make use of its unstructured data.
Despite technological advancements, the onboarding process is still a slog. Banks and vendors are trying to change that.
The quant firm’s Seth Weingram lays out his principles for the effective use of machine learning.
The bank is also looking at using AI for intelligent IOI suggestions based on clients’ trading profiles.
The pace of change—in politics and in fintech—over the past few years has been breathtaking. Jo finds out at NAFIS, however, that some things never change.
Machine learning can tell stories from large datasets to drive alpha, say quants.
The company is leveraging AI to make investment suggestions and dig out sentiment from spoken announcements.
As ESG data becomes more of a commodity, firms are struggling with how best to incorporate carbon data.
The firm is developing machine learning models internally to optimize the reconciliation process and detect price anomalies.