Machine learning
Natixis creates model to ‘learn’ how factors interact
Random forest technique sheds light on flux in how factors mix, manager says
Banks use machine learning to ‘augment’ corporate sales
Big banks are embarking on massive projects to tie up machine learning and big data to sell better to clients
Machine learning enters battle against financial crime
Standard Chartered and Barclays using AI to detect money laundering violations
Honesty is key to machine learning’s future – Roberts
Oxford-Man Institute director on why tomorrow’s models will gracefully admit defeat
Quants ‘running into walls’ with AI interpretability
Some firms “stumbling” with new technology, conference hears
Quants say big data is all buzz, no alpha
Efforts to extract alpha from alternative data have been “really unsuccessful”, says Domeyard’s Qi
Quants use AI to cut through murk of ‘sustainability’
Separating the wheat from the chaff is fundamental to ESG investing. Machine learning can do that
Model risk chiefs warn on machine learning bias
ML model outputs open to “potential bias sitting in your datasets”, says RBS model risk head
Blazing new analytical paths: Tackling data aggregation for new risk insights
As the risk function’s influence continues to grow within financial services firms, demand for quality integrated risk data to support a wider range of business-critical decisions is stretching the capabilities of existing technology to breaking point. A…
Data mining, machine learning and problems with autocalls
The week on Risk.net, January 19–25, 2019
Quant guide 2019: industry entrants face cultural ‘abyss’
Divide between industry and academia worries practitioners and professors
Arnott, Harvey: machine learning dangerous when data thin
Experts warn ML should be used “for its correct purpose”
HSBC and the risk-advisory robot
Bank has amassed 10-petabyte pool of client data to spot hedging, financing and payments needs
Credit risk quants are hitting the tech gap
An appetite to cut the costs of IRB is constrained by tougher regulatory scrutiny
Taking the lead on financial crime regulatory compliance
Increased scrutiny of anti-money laundering and customer due-diligence procedures means banks must create more efficient and effective systems. A recent webinar conducted by Risk.net and IBM discussed how leading banks are utilising artificial…
Learning algos that learn how to learn
Knowing what to remember and what to forget could help machines beat quant and discretionary investors
Degree of influence: are machines starting to learn finance?
This year's analysis recognises a turning point in machine learning applications
Buy-side quant of the year: Gordon Ritter
Risk Awards 2019: Quant uses new tech to tackle old problem of optimal execution
Global perspectives on operational risk management and practice: a survey by the Institute of Operational Risk (IOR) and the Center for Financial Professionals (CeFPro)
This paper presents survey results which represent comprehensive perspectives on operational risk practice, obtained from practitioners in a wide range of countries and sectors.
The machine shines in Hong Kong A-share fund
Strategy run by ChinaAMC (HK) combines machine learning with human judgement to outdo rivals
Basel’s archaic op risk taxonomy gets a makeover
Industry moves to revise out-of-date categories that feature risks such as cheque fraud
Quant of the year: Alexei Kondratyev
Risk Awards 2019: A glimpse of the future? Quant uses ML to model term structure and crunch margin costs
Asset manager of the year: Goldman Sachs Asset Management
Risk Awards 2019: Firm’s algos pick through earnings call transcripts to figure out what analysts really think