Credit Risk Modeling with Machine Learning

Credit Risk Modeling with Machine Learning

What a real-world machine learning solution looks like — no background knowledge required. In this article, we’ll explore from the ground up how machine learning is applied to credit risk modeling. You don’t need to know anything about machine learning to understand this article!

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Credit risk modeling–the process of estimating the probability someone will pay back a loan–is one of the most important mathematical problems of the modern world. In this article, we’ll explore from the ground up how machine learning is applied to credit risk modeling. You don’t need to know anything about machine learning to understand this article!

To explain credit risk modeling with machine learning, we’ll first develop domain knowledge about credit risk modeling. Then, we’ll introduce four fundamental machine learning systems that can be used for credit risk modeling:

  • K-Nearest Neighbors
  • Logistic Regression
  • Decision Trees
  • Neural Networks

By the end of this article, you’ll understand how each of these algorithms can be applied to the real-world problem of credit risk modeling, and you’ll be well on your way to understanding the field of machine learning in general!

Let’s begin learning about what credit risk modeling is by looking at a simple situation.

data-science machine-learning credit-risk-analysis

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