Unveiling Financial Stability: Deep Learning for Credit Scoring and Default Prediction

In the ever-evolving landscape of finance, credit scoring and default prediction remain critical for assessing the financial stability and creditworthiness of individuals and institutions. With the advent of deep learning, the ability to accurately predict credit defaults has significantly improved, providing financial institutions with powerful tools to mitigate risks. In this tutorial, we will delve into the application of deep learning for credit scoring and default prediction, using real financial data to build a predictive model.

Photo by Christopher Gower on Unsplash

Prerequisites

Before we begin, ensure that you have a basic understanding of Python programming, deep learning concepts and financial terminologies. Familiarity with libraries such as numpy, pandas, matplotlib, scikit-learn and tensorflow will be beneficial.

Setting Up the Environment

First, we need to set up our Python environment by installing the necessary libraries. Open your terminal or command prompt and execute the following commands:

pip install yfinance
pip install numpy
pip install pandas
pip install matplotlib
pip install scikit-learn
pip install tensorflow
pip install mplfinance

Importing Libraries