Analytics & Models
Startup helps buy-side firms retain ‘control’ over analytics
ExeQution Analytics provides a structured and flexible analytics framework based on the q programming language that can be integrated with kdb+ platforms.
BoE model risk rule may drive real-time monitoring of AI
New rule requires banks to rerun performance tests on models that recalibrate dynamically.
Microsoft, Google highlight LLM capabilities for future growth
Satya Nadella and Sundar Pichai both touted the advancements their respective companies have made in the field of generative AI during their earnings calls.
This Week: LSEG/Quantile, Ice, Morningstar/Sentifi, SimCorp/FundApps, and more
A summary of some of the past week’s financial technology news.
Bloomberg deploys math, not AI, to blend risk management and portfolio construction
The Mac3 GRM risk solution is live for equities users, uses no AI or machine learning, and will be rolled out to more asset classes next.
Goldman tackles climate risk controls
Lender joins other banks in translating physical and transition threats into controls framework
Waters Wavelength Podcast: Bloomberg on FRTB
Bloomberg's Eugene Stern and Brad Foster discuss data challenges relating to the Fundamental Review of the Trading Book (FRTB).
As asset managers look to Asia for alpha, analytics & visualization tools take center stage
PineBridge Investments uses economic and time-series data analytics provider Macrobond Financial to better its economics research, which supports its portfolio managers and the firm’s overall investment thesis.
Qontigo Releases New 'Linked' Risk Model Covering Global Equities
The new offering blends existing Axioma risk models into a single, nuanced risk assessment.
On Democracy and Alt Data's Democratization: Preparing For the US Election
Advancements in modeling and the rise of alt data have made the process of prepping for the US presidential election more complex, but hopefully more accurate.
Banks Struggle to Manage Technical Debt When Dealing with AI, Data Science
Data scientists, IT teams, and the business professionals should work together when deploying emerging technologies and data science models. Otherwise, they may be setting themselves up to fail.
AI Explainability 'an Afterthought' at Banks, But Financial Theory Infusion Could Help
Eric Tham of the National University of Singapore said during the Innovation Exchange that explainability is an afterthought at banks when they develop their AI-driven models. Unsurprisingly, some bankers did not agree.
Silent Eight Preps Transaction Monitoring Tool
The firm is testing the tool with a few clients before making it available to a broader audience.
Boosted.ai Rolls Out New Models for Navigating Covid-Specific Risks
As Covid-19 impacted companies and markets in March, the machine-learning startup sought to help clients better manage risk exposures that couldn't be explained by traditional risk factors.
Refinitiv Labs Preps for SentiMine Release
About two dozen Refinitiv clients have early access to the tool.
Machine Learning: A Math Problem or a Workflow Problem?
For good reason, machine learning has a highly technical focus. But less talked-about challenges lie in managing the human capital and workflows associated with the tech.
Ping An: NLP Takes on Greater Importance in Turbulent Times
The firm's chief scientist discusses how NLP is being used to prevent the spread of the coronavirus and how it can be applied for financial services.
Nasdaq's Lessons in Machine Learning Yield New Surveillance Tool
The offering, which took more than a year to build, combines deep, transfer and human-in-the-loop loop learning to find patterns.
DBS Bank Grows its Team of Data Translators
The bank is looking to pair this relatively new role with its data scientists as a bridge for business professionals.