Machine learning
Risk management strategies for revenue maximisation in financial services
A white paper discussing proactive risk management in financial services, focusing on automated solutions and artificial intelligence/machine learning technologies to enhance revenue assurance and operational efficiency
BMO’s cloud migration strategy eases AI adoption
Canadian bank is beginning to roll out GenAI tool for internal use cases
Execs can game sentiment engines, but can they fool LLMs?
Quants are firing up large language models to cut through corporate blather
How Ally found the key to GenAI at the bottom of a teacup
Risk-and-tech chemistry – plus Microsoft’s flexibility – has seen US lender leap from experiments to execution
Industry warns CFTC against rushing to regulate AI for trading
Vote on workplan pulled amid calls to avoid duplicating rules from other regulatory agencies
Top 10 operational risks for 2024
The biggest op risks for the year ahead, as chosen by senior industry practitioners
The bank quant who wants to stop GenAI hallucinating
Wells Fargo model risk chief thinks he has found a way to validate large language models
Quants are using language models to map what causes what
GPT-4 does a surprisingly good job of separating causation from correlation
Iosco gears up for ‘intensive work’ on AI regulation
Watchdogs risk ‘falling behind the curve’, secretary-general warns; FSB also working on guidance
Digging deeper into deep hedging
Dynamic techniques and GenAI simulated data can push the limits of deep hedging even further, as derivatives guru John Hull and colleagues explain
How AI can give banks an edge in bond trading
Machine learning expert Terry Benzschawel explains that bots are available to help dealers manage inventory and model markets
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