DataRobot Blog

APRIL 29, 2022

The stakes in managing model risk are at an all-time high, but luckily automated machine learning provides an effective way to reduce these risks. However, after the financial crisis, financial regulators around the world stepped up to the challenge of reigning in model risk across the financial industry.

Risk 111

Risk Modeling IT Risk Management 111

IBM Big Data Hub

FEBRUARY 8, 2024

1 Slowly but surely, institutional investors started to recognize that companies could potentially improve financial performance and risk management by focusing on ESG issues like greenhouse gas emissions. The total—$639 billion—shed light on how shareholders were starting to invest out of principle versus strictly profit.

Measurement 77

Measurement Metrics Reporting Strategy 77

Cloudera

MAY 15, 2018

My narrower vision of the next advancement in analytics is driven (or biased) by my quantitative risk management background and the critical role that computational simulation capabilities have played in many advances in the world of finance. Derman (2016), Cesa (2017) & Bouchard (2018)). Mauro Cesa. “A Simudyne Webpage.

Machine Learning 42

Machine Learning Recreation/Entertainment Finance Big Data 42

bridgei2i

JULY 28, 2020

She’s the founder and CEO of StatWeather, a company, which was recognized as number one in climate technology globally in the year, 2017, by the Energy Risk Awards. We need people who can test. Not just that. Then, if the computer system goes down, then what do we do? Ria Persad, Founder & CEO StatWeather.

Enterprise 98

Enterprise Digital Transformation Insurance B2B 98

CIO Business Intelligence

MARCH 22, 2022

At Fractal, Tiwari will be responsible for the company’s digital transformation and overseeing IT operations, cybersecurity, and risk management. . Prior to joining Fractal, Tiwari was senior vice-president and global CISO at Airtel, where he set up the managed security services initiative Airtel Secure for Business.

Internet Publishing and Broadcasting 98

Internet Publishing and Broadcasting Digital Transformation Insurance Recreation/Entertainment 98

Cloudera

MAY 18, 2021

These interactions are captured and the resulting synthetic data sets can be analysed for a number of applications, such as training models to detect emergent fraudulent behavior, or exploring “what-if” scenarios for risk management. Value-at-Risk (VaR) is a widely used metric in risk management. Intraday VaR.

Risk 98

Risk Marketing Risk Management Data-driven 98

Domino Data Lab

APRIL 3, 2019

Also, while surveying the literature two key drivers stood out: Risk management is the thin-edge-of-the-wedge ?for Adds governance, discovery, and access frameworks for automating the collection, management, and use of metadata. That definition plus the one-liner provide good starting points. for DG adoption in the enterprise.

Data Governance 84

Data Governance Machine Learning Metadata Big Data 84