Machine learning
How Man Numeric found SVB red flags in credit data
Network analysis helps quant shop spot concentration and contagion risks
Sovereign credit risk modeling using machine learning: a novel approach to sovereign credit risk incorporating private sector and sustainability risks
The authors investigate the effect of spillover effects from private sector risks on sovereign debt risk and the impact of rising sustainability risks on sovereign credit risk using the XGBoost classification algorithm and model interpretability…
Model risk management is evolving: regulation, volatility, machine learning and AI
Thomas Oliver, head of model validation at Quantifi, explores how the model risk management (MRM) landscape is changing in response to geopolitical uncertainty, increased concerns over counterparty risk, rising interest rates and other related challenges
BloombergGPT: Terminal giant enters the LLM race
New large language model aims to supercharge Terminal’s ability to provide sentiment, charting and search
OK regulator? How AI became respectable for AML controls
Dutch court case pressures supervisors to accept new tech; explainability the key challenge
The haves and the ‘have bots’: can AI give vol forecasters an edge?
Firms look to machine learning and natural language processing to gain advantage over peers
Momentum transformer: an interpretable deep learning trading model
An attention-based deep learning model for trading is presented
Can algos collude? Quants are finding out
Oxford-Man Institute is among those asking: could algorithms gang up and squeeze customers?
Machine learning for categorization of operational risk events using textual description
The authors summarise ways that machine learning can help categorize textual descriptions of operational loss events into Basel II event types.
Forecasting the loss given default of bank loans with a hybrid multilayer LGD model by extending multidimensional signals
The authors employ signaling theory and machine learning methods to investigate loss given default predictions of commercial banks and propose a method to improve the accuracy of these predictions.
Bot’s job? Quants question AI’s model validation powers
But supervisors cautiously welcome next-gen model risk management
Asset allocation with inverse reinforcement learning
Using reinforcement learning to help replicate asset managers' allocation strategy
Imbalanced data issues in machine learning classifiers: a case study
The author outlines characteristics of machine learning classifiers, compares methods for dealing with imbalanced data issues, and proposes terms of best practice in model development, evaluation, and validation.
Machine learning and AI in model risk management: a quant perspective
Statistical risk models face issues of validity as unprecedented events and social phenomena occur. However, artificial intelligence (AI) and machine learning can assist models in maximising accuracy. By Tiziano Bellini, head of risk integration…
Explainable artificial intelligence for credit scoring in banking
The authors put forward an explainable machine learning model predicting credit default using a real-world data set provided by a Norwegian bank.
Citi quants’ AI model aims to hedge earnings surprises
Machine learning tool forecasts effect of shocks on implied volatility surfaces in minutes
Navigating the complex world of equity options data
In an exclusive Risk.net webinar, convened in collaboration with Cboe Global Markets, experts discussed the expanding world of equity options data, the rise of retail investment within it, and the technological challenges and opportunities associated…
Alternatives to deep neural networks in finance
Two methods to approximate complex functions in an explainable way are presented
Front-office reboot: how new technology, machine learning and data science are reshaping trading
With increasing regulatory scrutiny and market volatility, trading desks are seeking tools to help improve operational efficiency, streamline decision making and successfully manage risk. In this Risk.net webinar, viewers will learn about the front…
How to hone NLP’s detection skills: a cue from a super-sleuth
With a sharper focus, AI readers could help detect hidden exposures for investors
Fraud prevention solution of the year: Quantexa
Asia Risk Awards 2022
Best AI or machine learning innovation: Quantifi
Asia Risk Awards 2022
Machine learning models: the validation challenge
Machine learning models are seeing increasing demand across the capital markets spectrum. But how can firms improve their chances of gaining internal and regulatory approval for these type of models?