Remove Data Processing Remove Measurement Remove Risk Management Remove Testing
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How to Gain Greater Confidence in your Climate Risk Models

Cloudera

Firms face critical questions related to these disclosures and how climate risk will affect their institutions. What are the key climate risk measurements and impacts? Stress testing was heavily scrutinized in the post 2008 financial crisis. in partnership with Deloitte, to accurately measure climate risk.

Risk 78
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The history of ESG: A journey towards sustainable investing

IBM Big Data Hub

It refers to a set of metrics used to measure an organization’s environmental and social impact and has become increasingly important in investment decision-making over the years. In response, asset managers began to develop ESG strategies and metrics to measure the environmental and social impact of their investments.

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Applying cyber resilience to DORA solutions

IBM Big Data Hub

The Digital Operational Resilience Act , or DORA, is a European Union (EU) regulation that created a binding, comprehensive information and communication technology (ICT) risk-management framework for the EU financial sector. Entities will also be expected to put appropriate cybersecurity protection measures in place.

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AI Technology is Invaluable for Cybersecurity

Smart Data Collective

As a result, businesses across many industries have been spending increasingly large sums on security technology and services, driving demand for trained specialists fluent in the latest preventative measures. After evaluating potential risks, cybersecurity professionals implement various preventative actions.

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What to Do When AI Fails

O'Reilly on Data

If our model generates false negative predictions for tumor detection, organizations could combine automated imaging results with activities like follow up radiologist reviews or blood tests to catch any potentially incorrect predictions—and even improve the accuracy of the combined human and machine efforts. How Material Is the Threat?

Risk 359
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3 areas where gen AI improves productivity — until its limits are exceeded

CIO Business Intelligence

We did side-by-side testing,” he says. In testing, gen AI was also particularly good at generating test cases and creating dummy data for testing. Still, he urges companies to look beyond measurements of coding speed. Eventually, he says, everyone will be using it, with AI tools being integral and reliable.

IT 124
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Why you should care about debugging machine learning models

O'Reilly on Data

In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. While our analysis of each method may appear technical, we believe that understanding the tools available, and how to use them, is critical for all risk management teams.