Credit Risk Customers

Keywords: credit, Risk

Background

Evaluating credit risk is a vital task for financial institutions as it helps determine the likelihood of clients defaulting on their credit obligations. Traditional methods for credit risk assessment typically rely on statistical models or rule-based systems. However, the emergence of machine learning techniques, particularly ANN, has provided a powerful tool for credit risk modeling. ANN excels at capturing intricate relationships and patterns within data. In this case, the developed ANN model adopts a supervised learning approach by training on historical credit data, using the target class feature to represent the credit risk of customers.

Aim & Objectives

Aim
To Predict the Credit Risk Customers Using DL Techniques
Objective
Develop a robust Artificial Neural Network (ANN) model to assess credit risk by leveraging historical credit data. The objective is to enhance the accuracy of credit risk assessment by utilizing the model's capability to capture complex patterns and relationships within the data, ultimately providing financial institutions with an advanced tool for predicting the likelihood of customer default and improving decision-making processes.

Research Methodology

ANN Technique used

Software & Tools

Jupyter, Google Collab

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