Deep Learning for Credit Risk Assessment: Beyond Traditional Scoring Models
Credit risk assessment is a critical task in the finance industry, where banks and financial institutions evaluate the risk of lending money to individuals or businesses. Traditionally, credit risk assessment has relied on statistical models and scoring systems to predict the likelihood of defaults or delinquencies.
However, with the advancements in deep learning and artificial intelligence, there is an opportunity to enhance credit risk assessment beyond traditional scoring models. Deep learning models can analyze complex patterns and relationships in large datasets, providing more accurate and reliable predictions of credit risk.
In this tutorial, we will explore how to leverage deep learning techniques to build a credit risk assessment model using Python. We will use the keras library to create a neural network model that can effectively evaluate credit risk based on various factors and features. By the end of this tutorial, you will have a deep understanding of how to apply deep learning in the finance industry to improve credit risk assessment processes.
Table of Contents
- Setting up the Environment
- Data Preprocessing
- Building the Model
