Machine learning
Seamless integration – Drivers of and barriers to cloud adoption
Siarhei Niaborski, executive vice-president of risk at CompatibL, discusses the rate of cloud adoption in the capital markets industry and its possible drivers and barriers, how firms can derive maximum value from cloud usage and the criteria on which…
BlackRock, State Street, AB are making AI work in risk
Risk USA: Fund managers are using new technology to measure liquidity risk and spot their own errors
Morgan Stanley, Wells not sold on AI for credit scoring
Risk USA: Lenders warn on AI model risks and use of non-traditional data
Buy side builds bots to cut trade costs
With margins under pressure, investment firms are looking to accelerate automation push
Stress-testing to improve strategic decision‑making
Banking regulators remain focused on expanding and developing the range of stress-testing regimes across the globe to maintain stability, monitor emerging risks and avoid another financial crisis. Here, a forum of industry leaders discusses the evolution…
Energy25: Energy firms turn to next-generation analytics
FIS, Enverus and Eka take to the podium in the inaugural Energy25 technology rankings
Banks seek new value for their efforts
As regulatory stress tests evolve and a new age of stress-testing approaches, firms are looking to maximise value by making the most of scenario-based analytics. John Voigt, principal solutions manager at SAS, explores the importance to institutions of…
Machines can save 20% on routing orders, UBS says
Machine learning is navigating the labyrinth of venues, figuring out how best to land the trade
Libor transition and implementation – Special report 2019
A critical halfway stage has been reached on the Libor transition journey – at least in terms of timing. It’s just over two years since the UK’s top financial regulator called notice on the discredited benchmark. It’s also just over two years until the…
Computer says no: combating bias in machine learning models
Proposed US law on algo lending targets in-built discrimination, say modelling experts
Allianz Global Investors adopts NLP signals in equities
Move to tackle unstructured data starts with sell-side analyst reports
Deploying agile analytics in the fight against fraud
Financial firms are under pressure to tackle the widespread problem of financial fraud. As the speed, scale and sophistication of fraudulent activity grows, a panel of financial crime experts reveal how firms can develop an agile analytics capability to…
Harnessing AI to achieve Libor transition
Chris Dias, principal at KPMG, explains how the vast increase in accuracy that artificial intelligence (AI) offers when dealing with large volumes of complex agreements is crucial to exploring the market opportunities and mitigating the risks of the…
Machine learning, Deutsche auction and repo haircuts
The week on Risk.net, September 16–20, 2019
Neuberger sidesteps option gap risk using AI and credit card data
Random-forest algorithm run on retail transaction data forewarns of earning shocks
Some quant shops doomed to ‘struggle’ – López de Prado
Theory-first firms must modernise their methods or wither, says machine learning expert
ETF provider of the year: Yuanta SITC
Asia Risk Awards 2019
An advanced hybrid classification technique for credit risk evaluation
In this paper, the authors employ a hybrid approach to design a practical and effective CRE model based on a deep belief network (DBN) and the K-means method.
NLP sniffs out contracts harbouring Eonia as fallback
Test finds wide range of 4,000 Libor euro contracts examined could end up in the flagging Eonia rate
Pimco deploys neural net model in agency bonds
Old models for $7 trillion mortgage market overstate risk in some cases, asset manager says
Goldman improves execution ‘by 50%’ with new algos
Bank uses neural networks and other AI tools to cut slippage in stock trading
The machines are coming for your pricing models
Deep learning is opening up new frontiers in financial engineering and risk management
The rise of the robot quant
The latest big idea in machine learning is to automate the drudge work in model-building for quants