Artificial intelligence
An alternative statistical framework for credit default prediction
This study compares the gradient-boosting model with four other well-known classifiers, namely, a classification and regression tree (CART), logistic regression (LR), multivariate adaptive regression splines (MARS) and a random forest (RF).
Managing AML and fraud – A risky business requires a risk-based approach
This webinar explores how to meet AML and fraud management obligations while empowering core businesses to remain competitive and innovative
Operational risk – Unleashing the power of AI to mitigate financial crime and manage conduct risk
Big data, data mining, machine learning and artificial intelligence have revolutionised how industry manages and mitigates risk. In light of the Covid-19 pandemic, what impact has this had on financial crime, what risks does remote working pose and how…
To model the real world, quants turn to synthetic data
Future financial models will be built using artificially generated data
Global macro views combine with quantitative models to produce consistent returns
The team behind River and Mercantile Group’s global macro strategy team operates under two key principles: that macro is the most important aspect of any investment decision and that decision-making should incorporate both systematic and discretionary…
How Goldman’s algos adapted to virus vol
Interview: Ralf Donner explains why algo usage is up while markets are down
Covid-19 tumult is testing AI fund returns
Some ML strategies have coped well, but others began to struggle as panic mounted
Building a holistic GRC framework in fragmented Asia-Pacific markets
This webinar explores best practices in meeting regulatory and data governance requirements in fragmented markets
Deep learning calibration of option pricing models: some pitfalls and solutions
Addressing model calibration and the issue of no-arbitrage in a deep learning approach
Lighting up the black box: a must for investors?
Many contend you must be able to interpret machine learning in order to use it
At Numerai, real-world figures need not apply
AI hedge fund CEO sees the light in black-box technology
Factor strategies seesaw in coronavirus-hit markets
Quants struggle to second-guess ongoing effect of virus on investments
Top 10 op risks 2020: regulatory risk
New technology and reams of red tape make non-compliance fines more likely
Top 10 op risks 2020: talent risk
Firms struggle to reduce headcount and fill gaps without cutting corners
‘Quantamental’ approach convinces Morgan Creek CEO
Proponent of big-picture investing sees growing role for machines, but with caveats
Treasurers turn to AI in bid for sharper forecasting
Wider automation could usher in future of ‘hands-free hedging’, but obstacles lurk in data standards and sharing
Exploring new investment prospects in volatile markets
Custom and traditional proprietary indexes have been growing in popularity and actively transforming the investment landscape. Financial products linked to indexes are thriving, enabling more efficient access to the market, whether it is equity, bonds or…
Singapore banks tighten ML governance amid regulatory scrutiny
DBS, StanChart and Deutsche build model inventories and draw up standards around use cases
No silver bullet for AI explainability
No single approach to interpreting a neural network’s outputs is perfect, so it’s better to use them all
Ripping up the old asset class labels
Outmoded classifications of securities may be concealing market risk. AI has a better idea
Disruption, CCP default and the latest Libor problem
The week on Risk.net, January 18–24, 2020
Bank disruptors: how tech joint ventures help Nomura’s bottom line
Nomura is developing new software services to supplement trading profits
Bank disruptors: SocGen’s call to start-ups
Fintechs and ‘intrapreneurs’ are leading Societe Generale’s digital transformation