Octopai

JULY 9, 2020

One example is the lineage methods that the banking industry has adopted to comply with regulations put in place following the 2007 financial collapse. It required banks to develop a data architecture that could support risk-management tools. A key piece of legislation that emerged from that crisis was BCBS-239.

Insurance 98

Insurance Risk Management Machine Learning Metadata 98

AWS Big Data

FEBRUARY 1, 2024

The following are some of the key business use cases that highlight this need: Trade reporting – Since the global financial crisis of 2007–2008, regulators have increased their demands and scrutiny on regulatory reporting. Apart from generating regulatory reports, these teams require visibility into the health of the reporting systems.

Data Warehouse 131

Data Warehouse Dashboards Risk Management Risk 131

Cloudera

FEBRUARY 15, 2024

It’s hard to believe it’s been 15 years since the global financial crisis of 2007/2008. From stringent data protection measures to complex risk management protocols, institutions must not only adapt to regulatory shifts but also proactively anticipate emerging requirements, as well as predict negative outcomes.

Insurance 88

Insurance Data Architecture Risk Management Risk 88

CIO Business Intelligence

JUNE 12, 2023

In 2012, COBIT 5 was released and in 2013, the ISACA released an add-on to COBIT 5, which included more information for businesses regarding risk management and information governance. It’s also designed to give senior management more insight into how technology can align with organizational goals.

Risk Management 94

Risk Management Strategy Digital Transformation Enterprise 94

Octopai

JUNE 16, 2021

If you’re a bank, however, taking risks doesn’t just have implications for you, but for all your customers and (if you’re big enough) for the economy as a whole. . Up-to-date data management makes meeting the credit risk and market risk standards much more feasible, and is absolutely necessary for operational risk.

Management 52

Management Risk Management Risk Visualization 52

Domino Data Lab

FEBRUARY 4, 2019

Eric’s article describes an approach to process for data science teams in a stark contrast to the risk management practices of Agile process, such as timeboxing. The ability to measure results (risk-reducing evidence). Frédéric Kaplan, Pierre-Yves Oudeyer (2007). Large-Scale Study of Curiosity-Driven Learning”.

Data Science 66

Data Science Experimentation Machine Learning Data-driven 66