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.

Machine Learning models have been helping these companies to improve the accuracy of their credit risk analysis, providing a scientific method to identify potential debtors in advance. In this article, we will build a model to predict the risk of client default for Nubank, a prominent Brazilian Fintech.

Learning is a new fun in the field of Machine Learning and Data Science. In this article, we’ll be discussing 15 machine learning and data science projects.

Most popular Data Science and Machine Learning courses — August 2020. This list was last updated in August 2020 — and will be updated regularly so as to keep it relevant

You will discover Exploratory Data Analysis (EDA), the techniques and tactics that you can use, and why you should be performing EDA on your next problem.

In this blog, we apply these algorithms to solve problems in financial mathematics such as credit risk analysis and option pricing. Finally, we conclude the series by briefly discussing our elementary toy quantum-model for forward contracts.