Machine learning
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Industry moves to revise out-of-date categories that feature risks such as cheque fraud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
Fed’s Brainard wary of black box AI models in consumer credit
Speech raises explainability issue; says existing model risk guidelines are “a good place to start” in regulating AI
AI data could be tainted even as it’s being cleaned
Risk USA: Expert says even touching raw data could lead to loss of context
Man embraces open source in push to lure tech talent
Risk USA: Forget Silicon Valley – come work in finance, hedge fund CRO tells technologists
BlackRock shelves unexplainable AI liquidity models
Risk USA: Neural nets beat other models in tests, but results could not be explained
Banks split on human oversight of AI models
Risk USA: Most firms supervise their models, but one expert says they can be trusted to make decisions
Humans struggle to keep pace with machine learning
Banks and regulators grapple with ‘XAI’ challenge
Machine learning hits explainability barrier
Banks hire AI industry experts in face of growing regulatory scrutiny
Why Dario Villani trusts machine learning
Duality Group CEO says people should abandon ‘top-down, godlike model’ and their need to understand
At BlackRock’s West Coast AI lab
The firm is handing its ‘most vexing problems’ to artificial intelligence
Predictive fraud analytics: B-tests
In this paper, the authors look at B-tests: methods by which it is possible to identify internal fraud among employees and partners of the bank at an early stage.
Big funds muzzle their AI machines
Fears over interpretability, crowding and overfitting have put a damper on efforts to unleash AI for asset management
