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 121

Data Warehouse Dashboards Risk Management Risk 121

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

CIO Business Intelligence

APRIL 28, 2022

auxmoney began as a peer-to-peer lender in 2007, with the mission of improving access to credit and promoting financial inclusion. Right from the start, auxmoney leveraged cloud-enabled analytics for its unique risk models and digital processes to further its mission.

Analytics 98

Analytics Insurance Big Data Uncertainty 98

Octopai

APRIL 8, 2020

The worldwide economy was shaken in 2007 when the United States stock market had its largest drop since the Great Depression. While there are many factors that led to this event, one critical dynamic was the inadequacy of the data architectures supporting banks and their risk management systems.

Finance 52

Finance Cost-Benefit Metadata Data Architecture 52

CIO Business Intelligence

DECEMBER 20, 2023

William Dettmer, The Logical Thinking Process: A Systems Approach to Complex Problem Solving, Milwaukee, Wisconsin, ASQ Quality Press, 2007 [2] Verizon Payment Security Report, 2022, pages 9 and 64-79, www.verizon.com/paymentsecurityreport [3] Eliyahu M.

Optimization 52

Optimization Data-driven Risk Management Management 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. As the article explains, data science is set apart from other business functions by two fundamental aspects: Relatively low costs for exploration. Yuri Burda, et al.

Data Science 66

Data Science Experimentation Data-driven Machine Learning 66