Artificial intelligence
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
Fighting financial fraud with AI
Risk and compliance professionals convened for a Risk.net webinar, How to successfully mitigate fraud – AI in action, in association with NICE Actimize to debate the use of artificial intelligence in the fight against fraud
DBS chief executive on fighting tech disruptors with data
Asia Risk 25: Data and AI are top technology priorities for Singapore bank, says Piyush Gupta
Machine learning will create new sales-bots – UBS’s Nuti
Technologists working to automate indications of interest from trading desks
HSBC exec: measure culture through smarter surveillance
Machine learning could help gauge positive sentiment from surveillance logs, says Elhedery
Model misfires raise questions over training data
Quants wrestle with how far into the past their machine learning models should peer
Regions deploys early-warning tool for credit risk
Risk USA: system alerted US superregional to impending defaults during Covid crisis
How to successfully mitigate fraud – AI in action
Fraud is evolving, with influences spanning technical sophistication through to turmoil and crisis. Most recently, the Covid-19 pandemic has thrown an additional spanner in the works. As the drivers behind these activities are becoming more varied, the…
Toward reducing the operational risk of emerging technologies adoption in central counterparties through end-to-end testing
This paper discusses the software-testing challenges of traditional central counterparties as well as the risks, biases and problems related to new technologies. It also outlines a set of requirements for an end-to-end validation and verification…
AML bill will swamp financial crime teams, banks warn
Proposed US legislation could force firms to run new and old systems in parallel, stretching resources
Banks warn of rise in ransomware attacks
OpRisk Europe: Banks must improve resilience of remote-working staff, says Wells Fargo financial crime expert
Alt data aims to shake up credit scoring business
Young firms, using machine learning methods to scrape consumer info, challenge established agency model
ETF provider of the year: Yuanta SITC
Asia Risk Awards 2020
Asset management firm of the year: Ping An of China Asset Management (Hong Kong)
Asia Risk Awards 2020
Managing AML and fraud – A risky business needs a risk-based approach
Financial services organisations (FSOs) are expected to meet strict financial crime regulations regardless of their size, and those with smaller budgets and fewer resources are finding this increasingly difficult as regulations, guidelines and threats…
Driving anti-money laundering efficiency gains using artificial intelligence
Anti-money laundering (AML) is expensive and labour-intensive, and artificial intelligence (AI) can offer improved efficiency gains. Could they be a match made in heaven? This Risk.net webinar, in association with NICE Actimize, took place amid the…
The changing shape of buy-side risk technology
This webinar shares insights into the emerging strategies shaping firms’ investment, compliance and technology risk decisions, and how Covid-19 is causing a rethink in priorities
A new risk era – Recovering stronger from the pandemic
Jose Ribas, global head of risk and pricing solutions at Bloomberg, discusses how risk management at financial institutions is changing in the wake of the pandemic and the subsequent volatility, the role of regulations and how technology can help risk…
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