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Model Risk Management And the Role of Explainable Models(With Python Code)

Analytics Vidhya

The post Model Risk Management And the Role of Explainable Models(With Python Code) appeared first on Analytics Vidhya. This article was published as a part of the Data Science Blogathon. Photo by h heyerlein on Unsplash Introduction Similar to rule-based mathematical.

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Managing risk in machine learning

O'Reilly on Data

As the data community begins to deploy more machine learning (ML) models, I wanted to review some important considerations. We recently conducted a survey which garnered more than 11,000 respondents—our main goal was to ascertain how enterprises were using machine learning. Let’s begin by looking at the state of adoption.

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

O'Reilly on Data

As companies use machine learning (ML) and AI technologies across a broader suite of products and services, it’s clear that new tools, best practices, and new organizational structures will be needed. Note that the emphasis of SR 11-7 is on risk management.). Sources of model risk. Model risk management.

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Why you should care about debugging machine learning models

O'Reilly on Data

For all the excitement about machine learning (ML), there are serious impediments to its widespread adoption. 1] This includes C-suite executives, front-line data scientists, and risk, legal, and compliance personnel. Not least is the broadening realization that ML models can fail. That’s where model debugging comes in.

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Can Machine Learning Address Risk Parity Concerns?

Smart Data Collective

Machine learning technology has already had a huge impact on our lives in many ways. There are numerous ways that machine learning technology is changing the financial industry. However, machine learning can also help financial professionals as well. What is risk parity? Who invented risk parity?

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XAI: Accuracy vs Interpretability for Credit-Related Models

Analytics Vidhya

When too much risk is restricted to very few players, it is considered as a notable failure of the risk management framework. […]. Introduction The global financial crisis of 2007 has had a long-lasting effect on the economies of many countries.

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New regulation intensifies focus on IT risk management and operational resilience

CIO Business Intelligence

As IT landscapes and software delivery processes evolve, the risk of inadvertently creating new vulnerabilities increases. These risks are particularly critical for financial services institutions, which are now under greater scrutiny with the Digital Operational Resilience Act ( DORA ).