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Loan Risk Analysis with Supervised Machine Learning Classification

Analytics Vidhya

Introduction to Classification Algorithms In this article, we shall analyze loan risk using 2 different supervised learning classification algorithms. The post Loan Risk Analysis with Supervised Machine Learning Classification appeared first on Analytics Vidhya.

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Deterministic vs Stochastic – Machine Learning Fundamentals

Analytics Vidhya

Deterministic and stochastic models are approaches in various fields, including machine learning and risk assessment. This article will explore the pros and cons of deterministic and stochastic models, their applications, and their impact on machine learning and risk assessment.

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Predicting Possible Loan Default Using Machine Learning

Analytics Vidhya

Borrowers who default on loans not only damage their credit but also risk being sued […]. The post Predicting Possible Loan Default Using Machine Learning appeared first on Analytics Vidhya. Introduction A loan default occurs when a borrower takes money from a bank and does not repay the loan.

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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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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.

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Breaking Down Social Bias in Artificial Intelligence Algorithms for Cardiovascular Risk Assessment

Analytics Vidhya

Cardiovascular disease (CVD) prevention is crucial for identifying at-risk individuals and providing timely intervention. However, traditional risk assessment models like the Framingham Risk Score (FRS) have shown limitations, particularly in accurately estimating risk for socioeconomically disadvantaged populations.

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A CIO primer on addressing perceived AI risks

CIO Business Intelligence

Ask your average schmo what the biggest risks of artificial intelligence are, and their answers will likely include: (1) AI will make us humans obsolete; (2) Skynet will become real, making us humans extinct; and maybe (3) deepfake authoring tools will be used by bad people to do bad things. Risks perceived by an average schmo 1.

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Trusted AI 102: A Guide to Building Fair and Unbiased AI Systems

The risk of bias in artificial intelligence (AI) has been the source of much concern and debate. These risks undermine the underlying trust in AI and affect your organization’s ability to deliver successful AI projects, unhindered by potential ethical and reputational consequences.

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How Banks Are Winning with AI and Automated Machine Learning

Estimating the risks or rewards of making a particular loan, for example, has traditionally fallen under the purview of bankers with deep knowledge of the industry and extensive expertise. By leveraging the power of automated machine learning, banks have the potential to make data-driven decisions for products, services, and operations.