Artificial intelligence
A survey of machine learning in credit risk
This paper surveys the impressively broad range of machine learning methods and application areas for credit risk.
Axes that matter: PCA with a difference
Differential PCA is introduced to reduce the dimensionality in derivative pricing problems
Model risk management: building trust and governance
As organisations increasingly rely on models that cover a wide range of business functions, there is an increasing need to create and maintain a comprehensive model inventory for enhanced collaboration and regulatory compliance across multiple regions…
Rethinking the model lifecycle: from quick fixes to long-term gain
Covid-19 has caused widespread disruption to banks’ risk models. Some failed in the crisis while others have required significant overlays or frequent recalibration as extreme volatility has given way to ongoing uncertainty. As banks seek more agile…
AI helps one investor screen targets against UN ethical goals
PanAgora develops two-stage process that aims to weed out the greenwashers
Wells touts new explainability technique for AI credit models
Novel interpretability method could spur greater use of ReLU neural networks for credit scoring
Show your workings: lenders push to demystify AI models
Machine learning could help with loan decisions – but only if banks can explain how it works. And that’s not easy
Operational resilience 3.0 – Unlocking potential and elevating response
In a Risk.net webinar convened in collaboration with Fusion Risk Management, an expert panel delved into best practices for businesses to elevate systems to the next level of prediction, preparation and protection
AML models face explainability challenges
Data gaps and potential biases must be accounted for in approaches to tackling money laundering
The race to digital resiliency – Building a robust data management framework
Addressing privacy concerns is not a new topic for data and analytics, but with the explosion of regulations and growing consumer concern around how data can and cannot be used, addressing compliance requirements is more important than ever
Goal-based wealth management with reinforcement learning
A combination of machine learning techniques provides multi-period portfolio optimisation
How algos are helping inflation-wary investors
Buy-siders look to machine learning for clues on the effect of rising prices on portfolios
Zurich’s Scott: don’t levy climate risk capital charges
Imposing set-asides based on stress tests “does not make any sense”, sustainability chief warns watchdogs
Banks fear Fed crackdown on AI models
Dealers say agencies’ request for info could prompt new rules that stifle model innovation
Driving value from GRC
In today’s fast-changing business environment, an effective governance, risk and compliance (GRC) programme is increasingly seen as a foundation of agile decision-making. Michael Gibbs, chief executive officer of SureStep Systems Integration, discusses…
Acadian builds ‘green screen’ to auto-filter ESG phoneys
$110 billion quant investor creates automated system to spot greenwashers
BNP Paribas AM turns to machine learning for carbon emissions
AI may help fund manager count emissions that companies fail to report
From use cases to a big data benchmarking framework in clearing houses and exchanges
In this paper, we propose a conceptual framework that links the technical and business benchmarks in the domain of clearing houses and securities exchanges.
The importance of getting technology change right
Christoph Kurth, partner and member of the global financial institutions leadership team at Baker McKenzie, covers some of the rapid technological changes under way brought about by, and in the wake of, the Covid-19 pandemic
As machines disrupt investing, people still have a role to play
Despite AI’s growth, investing still needs human adaptability and judgement, writes Schroders’ Lim
A hybrid model for credit risk assessment: empirical validation by real-world credit data
This paper examines which hybridization strategy is more suitable for credit risk assessment in the dynamic financial world.
Ex-SunGard chief Cris Conde’s random walk to fintech and beyond
Technologist talks artificial intelligence, angel investing and accidentally contributing to the Basel framework
Technology innovation of the year: Scotiabank
Risk Awards 2021: new risk engine can run nearly a billion XVA calculations per second