Machine learning
Shapley values as an interpretability technique in credit scoring
The authors analyze the usefulness of the Shapley value as a machine learning interpretability technique in credit scoring.
Will generative AI crack the code for bank tech teams?
Banks could roll out tools to help translate old – or write new – code within months
Quant shop preps NLP-powered index for physical climate risk
Sharp rise in extreme weather events prompts PGIM Quant to aim for better climate-risk pricing
AllianceBernstein: fine-tuning shrinks GenAI ‘hallucinations’
Asset manager says its tweaks have improved accuracy of LLM models
AI model uses quantum maths to learn like a human
Could the next big breakthrough in machine learning come from the world of finance?
Can machine learning help predict recessions? Not really
Artificial intelligence models stumble on noisy data and lack of interpretability
FRAML solutions 2023: market and vendor landscape
As concerns around financial crime escalate, financial institutions and regulators are placing greater emphasis on combined FRAML solutions. For financial institutions the benefits of an integrated FRAML platform include improved capabilities,…
AI in risk management: one giant leap forward or a risk too far?
As technology advances at lightning speed, AI brings its own, not inconsiderable, risks. How, then, are today’s risk managers using AI tools to their best advantage – and what threats do they face along the way? In a Risk.net webinar, sponsored by FIS,…
Generative AI is changing debate on explainability, says Deutsche
Innovation head says observability can aid regulatory acceptance
Navigating the adoption of generative AI
This white paper, created by Xoriant, focuses on generative artificial intelligence (AI) and its potential to transform the way financial services firms operate, make business decisions and innovate.
Quants look to language models to predict market impact
Oxford-Man Institute says LLM-type engine that ‘reads’ order-book messages could help improve execution
JP Morgan pulls plug on deep learning model for FX algos
US bank turns to less complex models that are easier to explain to clients
Revolutionising credit decisioning in digital banking: harnessing AI/machine learning, LLM and alternative data sources
This webinar explores how financial institutions can leverage artificial intelligence, machine learning, large language models and alternative data sources, including open banking data, to modernise credit risk assessment and application fraud prevention
Getting more for less: better A / B testing via causal regularisation
A causal machine learning algorithm is used to estimate trades’ price impact
Understanding and predicting systemic corporate distress: a machine-learning approach
The authors construct a machine-learning-based early-warning system to predict, one year in advance, risks of systemic distress and demonstrate factors which can predict corporate distress.
Toward a unified implementation of regression Monte Carlo algorithms
The authors put forward a publicly available computational template for machine learning, named mlOSP, which presents a unified numerical implementation of RMC approaches for optimal stopping.
How a machine learning model closed a hidden FX arbitrage gap
MUFG Securities quant uses variational inference to control the mid volatility of options
Citi cyber chief says AI providing new weapons in hacking wars
Barron-DiCamillo also urges regulators to work with industry best practice, not against it
Obtaining arbitrage-free FX implied volatility by variational inference
An ML-based algorithm that provides implied volatilities from bid-ask prices is proposed
Optical computer beats quantum tech in tricky settlement task
Microsoft’s analog technology twice as accurate compared to IBM’s quantum kit in Barclays experiment
The chatbot and the quant: GPT shakes finance education
With smarter large language models, quant grads risk turning into AI-assisted slackers, writes Gordon Lee
Benchmarking machine learning models to predict corporate bankruptcy
Based on a comprehensive sample, the authors benchmark machine learning models in the prediction of financial distress of publicly traded US firms, with gradient-boosted tress outperforming other models in one-year-ahead forecasts.
FCA may offer its market data to surveillance tech start-ups
Risk Live: Regulator concerned rapid AI adoption will favour incumbent vendors; aims to launch sandbox
UBS found no advantage in quantum computing – ex data chief
Swiss bank tested various use cases in the trading business before giving up on the technology