Articles & Video
How are quants addressing new regulation and technology?
Quantum computing, AI & ML fairness, IBOR reform
From investment strategies to tech and regulation: what did the experts at QuantMinds International say?
What’s being discussed at QuantMinds International? Our speakers joined us for a quick Q&A to talk about key issues in their fields of expertise.
AI explainability and adoption challenges: Thoughts from the AI & ML Summit
What’s standing in the way of artificial intelligence adoption?
From scarce to large data and back: machine learning in finance
Fuelled by data, machine learning methods require an efficient handling of massively large datasets. But what are the challenges posed by datasets themselves?
Fairness in AI and machine learning
How can financial institutions mitigate issues in AI & ML bias and maintain public trust?
The economic implications of climate change
We examine the physical risk of climate change for each country using the Moody’s Analytics Global Macroeconomic Model.
The future is bright... for those who survive
Last year we caught up with Marcos Lopez de Prado, talked about his book Machine Learning For Asset Managers, and predicting black swan events.
Derisking AI by design: How to build risk management into AI development
The compliance and reputational risks of artificial intelligence pose a challenge to traditional risk-management functions. Derisking by design can help.
3 trends that will change quants’ future
Few things we learnt from QuantMinds International 2020.
Building the future of quant finance
Key insights from QuantMinds International 2020
Analysing Covid-19 Data with AWS Data Exchange, Amazon Redshift, and Tableau
How to create COVID-19 dashboards using Tableau and different AWS services, such as AWS Data Exchange, AWS COVID-19 Data Lake, Amazon Redshift, and Amazon Athena.
Truly Explainable AI: Putting the “Cause” in “Because”
There are two serious problems with state-of-the-art machine learning approaches.
Model robustification and the future of quant finance: an interview with J.D. Opdyke, Allstate
Were your models caught by surprise when the pandemic hit? J.D. Opdyke, Allstate, explains how he built a robust framework and shares key learnings from this year.
How is reinforcement learning different from un/supervised learning?
Marshall Chang, Founder and CIO, A.I. Capital Management, shares how to develop and deploy RL trading algorithms.
Staying afloat: How is quant finance changing under Covid-19?
Is the worst behind us? Or is it still to come?
Beyond Weisfeiler-Lehman: using substructures for provably expressive graph neural networks
How powerful are graph neural networks?
Five issues quants need to address in 2020
QuantMinds editor-in-chief Vincent Beard now summarises the five key conundrums on quants’ minds this year.
Fourier-based methods for the management of complex insurance products
Modelling for variable annuities products – how could the financial and insurance risks be integrated better?
Differential machine learning
Unreasonably effective pricing and risk approximation by automatic differentiation (AAD) combined with machine learning (ML)
A conversation with “Quant of the Year 2019” Marcos Lopez de Prado
Marcos Lopez De Prado opens up about the issues of econometric investments and how asset management needs new technological innovations.