Articles & Video
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
How should banks be handling the IBOR transition?
An interview with Suman Datta, Head of portfolio, Quantitative research, Lloyds Bank
QuantMinds 2021 calendar
Virtual learning opportunities and in-person get togethers for the quant finance community.
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 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.
Optimising variable annuity reserves through deep hedging
In this white paper we look at an application of the deep hedging approach to a new business problem: optimising reserves that life insurers are required to hold against variable annuity liabilities.
The rising awareness of model risk
Financial institutions are increasingly reliant on statistical risk models. But in turn, what risks do models pose to financial institutions?
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?
Can FX hedges of bonds deliver a free lunch?
Risk-free returns don’t exist – and if they did, even briefly, they would be traded on until they disappeared. Except sometimes, somehow, under just the right circumstances, the elusive free lunch may temporarily appear on the table…
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)
Can models predict black swan events?
Maurizio Garro, Senior Lead IBOR Transition Programme, Lloyds Bank, shares his experience building robust models to predict the combined impact of risk factors.