Remove Predictive Modeling Remove Reporting Remove Risk Management
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Why you should care about debugging machine learning models

O'Reilly on Data

That’s where model debugging comes in. Model debugging is an emergent discipline focused on finding and fixing problems in ML systems. In addition to newer innovations, the practice borrows from model risk management, traditional model diagnostics, and software testing. Sensitivity analysis.

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Minding Your Models

DataRobot Blog

At many organizations, the current framework focuses on the validation and testing of new models, but risk managers and regulators are coming to realize that what happens after model deployment is at least as important. Legacy Models. No predictive model — no matter how well-conceived and built — will work forever.

Modeling 105
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Transforming IT from cost center to catalyst

CIO Business Intelligence

Studies like Foundry’s 2024 State of the CIO report reveal a dramatic change in attitude. My experience aligns with this trend. We envisioned harnessing this data through predictive models to gain valuable insights into various aspects of the industry. Risk management is essential, but it shouldn’t stifle innovation.

IT 122
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CDOs: Your AI is smart, but your ESG is dumb. Here’s how to fix it

CIO Business Intelligence

Limited representation of sustainability in CDO priorities A review of industry reports, surveys and conference agendas suggests that sustainability remains a niche topic within the data leadership community. Most data management conferences and forums focus on AI, governance and security, with little emphasis on ESG-related data strategies.

IT 59
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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.

Risk 111
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Generative AI copilots: What’s hype and where to drive results

CIO Business Intelligence

CIOs must also partner with CISOs, legal, human resources, and business leaders to build awareness of policies and develop a generative AI risk management strategy. While that’s a limitation, there are reports of promised functionality not yet available.

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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. Risk Monitoring and Communication. Risk Quantification and Analytics. banking, insurance and securities) measure risk on a quantitative basis.