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How Financial Services and Insurance Streamline AI Initiatives with a Hybrid Data Platform

Cloudera

The way to manage this is by embedding data integration, data quality-monitoring, and other capabilities into the data platform itself , allowing financial firms to streamline these processes, and freeing them to focus on operationalizing AI solutions while promoting access to data, maintaining data quality, and ensuring compliance.

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What is Model Risk and Why Does it Matter?

DataRobot Blog

With the big data revolution of recent years, predictive models are being rapidly integrated into more and more business processes. This provides a great amount of benefit, but it also exposes institutions to greater risk and consequent exposure to operational losses. Model Validation – Prior to the use of a model (i.e.,

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20 for 20: IRM Critical Capabilities & Top 20 Functions / Features

John Wheeler

We continue our “20 for 20” theme this year by highlighting the integrated risk management (IRM) critical capabilities and top 20 software functions / features. Beyond assessing risk from a qualitative perspective, companies in many industries (e.g.,

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What to Do When AI Fails

O'Reilly on Data

All predictive models are wrong at times?—just As the renowned statistician George Box once quipped , “All models are wrong, but some are useful.” Broadly speaking, materiality is the product of the impact of a model error times the probability of that error occuring. just hopefully less so than humans.

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