Remove 2008 Remove Reporting Remove Risk Remove Statistics
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What is Model Risk and Why Does it Matter?

DataRobot Blog

This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. 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.

Risk 111
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AI Data, Traditional Trading, and Modern Investments

Smart Data Collective

Thanks to the invention of the internet, everything from conducting trades to downloading comprehensive reports can be completed almost instantly. By analyzing, identifying, and predicting these trends, analysts are able to help their clients minimize risk while enjoying large returns.

Finance 138
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Managing machine learning in the enterprise: Lessons from banking and health care

O'Reilly on Data

After the 2008 financial crisis, the Federal Reserve issued a new set of guidelines governing models— SR 11-7 : Guidance on Model Risk Management. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Machine learning developers are beginning to look at an even broader set of risk factors.

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Reclaiming the stories that algorithms tell

O'Reilly on Data

These scores go on student report cards, and are a frequent topic at parent-teacher conferences. Ever since 1989, the state has periodically published a report card that rates each surgeon, by name, based on how many of that surgeon’s patients died in hospital or within 30 days after coronary artery bypass surgery.

Risk 355
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Our quest for robust time series forecasting at scale

The Unofficial Google Data Science Blog

Such a model risks conflating important aspects, notably the growth trend, with other less critical aspects. In other words, there is an asymmetry of risk-reward when there exists the possibility of misspecifying the weights in $X_C$. This imbues the forecasting routine with two attractive properties.

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New Thinking, Old Thinking and a Fairytale

Peter James Thomas

Of course it can be argued that you can use statistics (and Google Trends in particular) to prove anything [1] , but I found the above figures striking. Feel free to substitute Data Lake for Data Warehouse if you want a more modern vibe, sadly it won’t change the failure statistics. . [5]. Source: Google Trends.

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Data Science, Past & Future

Domino Data Lab

He was saying this doesn’t belong just in statistics. It involved a lot of work with applied math, some depth in statistics and visualization, and also a lot of communication skills. You see these drivers involving risk and cost, but also opportunity. Tukey did this paper. It’s a great read. You know what?