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Transforming FSI in ASEAN with Cloud Analytics

CIO Business Intelligence

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. compounded annual growth from 2019 to 2024. .

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Combine transactional, streaming, and third-party data on Amazon Redshift for financial services

AWS Big Data

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.

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Misleading Statistics Examples – Discover The Potential For Misuse of Statistics & Data In The Digital Age

datapine

Drinking tea increases diabetes by 50%, and baldness raises the cardiovascular disease risk up to 70%! Did we forget to mention the amount of sugar put in the tea or the fact that baldness and old age are related – just like cardiovascular disease risks and old age? Transparency and Data-Driven Business Solutions. They sure can.

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Modernize Using The BI & Analytics Magic Quadrant

Rita Sallam

Like when Oracle acquired Hyperion in March of 2007, which set of a series of acquisitions –SAP of Business Objects October, 2007 and then IBM of Cognos in November, 2007. Sisense supports a wide range of relational, NoSQL and big data sources. In BI we have had our seminal moments too.

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

Here we come back to the upward trend in searches for Data Science. It could be argued of course that this is yet another business fad (indeed some are speaking about Big Data in just those terms already [7] ), but I believe that there is more substance to the area than this. – Gartner 2007. “60-70%

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Changing assignment weights with time-based confounders

The Unofficial Google Data Science Blog

One reason to do ramp-up is to mitigate the risk of never before seen arms. A ramp-up strategy may mitigate the risk of upsetting the site’s loyal users who perhaps have strong preferences for the current statistics that are shown. Proceedings of the 13th ACM SIGKDD international conference on Knowledge discovery and data mining.

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Themes and Conferences per Pacoid, Episode 6

Domino Data Lab

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. Taking a pulse.