Machine learning
Why AI will never predict financial markets
Laws that govern swings in asset prices are beyond statistical grasp of machine learning technology, argues academic Daniel Bloch
Everything, everywhere: 15 AI use cases in play, all at once
Research is top AI use case, best execution bottom; no use is universal, and none shunned, says survey
Researchers, quants, strats – AI is coming for you
Survey IDs roles to be most impacted by front-office AI, but experts say many will change, not disappear
Front office open to AI promise
AI offers real potential for capital markets firms. But how disruptive, and how immediate, will the impact be?
For AI’s magic hammer, every problem becomes a nail
Risk.net survey finds banks embracing a twin-track approach to AI in the front office: productivity tools today; transformation tomorrow
Degree of influence 2024: volatility and credit risk keep quants alert
Quantum-based models and machine learning also contributed to Cutting Edge’s output
Quants try investing like Socrates, with help from AI
Researchers are testing whether LLMs can use methods borrowed from ancient philosophy to answer complex questions
How a serverless risk engine transformed a digital bank
Migrating to the cloud permitted scalability, faster model updates and a better team structure
Mitigating model risk in AI
Advancing a model risk management framework for AI/machine learning models at financial institutions
The prediction of mortgage prepayment risks in the early stages of loan origination: a machine learning approach
The authors put forward a machine learning model for the prediction of mortgage prepayment risks at the loan origination phase.
Advanced visualization for the quant strategy universe: clustering and dimensionality reduction
The authors present a novel visualisation model, based on 5000 quantitative investment strategies, which can identify nonlinear relationships and clustering strategies with similar risk factor exposures.
Best use of machine learning/AI: CompatibL
CompatibL won Best use of machine learning/AI at the 2025 Risk Markets Technology Awards thanks to its ground-breaking use of LLMs for automated trade entry, redefining speed and reliability in what-if analytics
Rising star in quant finance: Milena Vuletić
Risk Awards 2025: Machine learning-based volatility model confounds sceptics
Research on the multifractal volatility of Chinese banks based on the synthetic minority oversampling technique, edited nearest neighbors and long short-term memory
The authors propose the SMOTEENN-LSTM method to predict risk warnings for Chinese banks, demonstrating the improved performance of their model relative to commonly used methods.
A model combining Optuna and the light gradient-boosting machine algorithm for credit default forecasting
The authors put forward a default prediction model designed to make the analysis of complex, highly dimensional and imbalanced real-world bank data easier.
Banks must loosen up on ChatGPT use – risk chiefs
Risk Live: ’Shadow use’ and inability to attract new hires mean restricting access to GPTs is untenable
Quantum cognition machine learning: financial forecasting
A new paradigm for training machine learning algorithms based on quantum cognition is presented
Dutch regulator in new push on algo manipulation
AFM teams up with Oxford Uni academics to develop data models that will identify “harmful” activity in automated trading
Analyzing credit risk model problems through natural language processing-based clustering and machine learning: insights from validation reports
The authors use clustering and machine learning techniques to analyze validation reports, providing insights to the development, implementation and maintenance of credit risk models.
Machine learning prediction of loss given default in government-sponsored enterprise residential mortgages
The authors apply machine learning techniques to Loss Given Default estimation, identifying key variables in LGD prediction and evaluating the performance of various models.
Forecasting India’s foreign trade dynamics: evaluation of alternative forecasting models in the post-pandemic period
The authors aim to determine how India's foreign trade will change following Covid-19 and the Russia-Ukraine conflict, comparing several forecasting models and identifying that which performs best.