Add to ORKGBanking Industry always needs a more accurate predictive modeling system for many issues. Predicting credit defaulters is a difficult task for the banking industry. The loan status is one of the quality indicators of the loan. It does not show everything immediately, but it is a first step of the loan lending process. The loan status is used for creating a credit-scoring model. The credit-scoring model is used for accurate analysis of credit data to find defaulters and valid customers. The objective of this is to create a credit-scoring model for credit data. Various machine-learning techniques are used to develop the financial credit-scoring model. For this classification we use the ’Random Forest Algorithm’. This proposed provides the imp...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
This research uses data from Kaggle, which consists of 32,581 rows and 12 columns, to develop a cred...
Credit-lending companies have resorted to the use of Machine Learning algorithms in the recent past ...
Businesses depend on banks for financing and other services. The success or failure of a company dep...
Giving credit is one of the core businesses in banking and the importance of credit risk management ...
As the loan is one of the most important products of banking and financial companies who have intere...
Although there are various items that banking systems can sell to make money, their primary source o...
As people's consumption habits change, loan plays a crucial role in our modern society. It provides ...
Credit risk plays a major role in the banking industry business. Banks' main activities involve gran...
The failure or success of the banking industry depends largely on the industrys ability to properly ...
This research contains the discussion the use of machine learning for doing prediction toward a good...
Classification is a powerful tool in Data mining to predict the loan repayment capability of a banki...
Masters Degree. University of KwaZulu-Natal, Durban.Loan lending has become crucial for both individ...
Loans play a crucial role in the financial industry, and predicting the likelihood of a borrower's a...
This master thesis explore the potential of Machine Learning techniques in predicting default of veh...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
This research uses data from Kaggle, which consists of 32,581 rows and 12 columns, to develop a cred...
Credit-lending companies have resorted to the use of Machine Learning algorithms in the recent past ...
Businesses depend on banks for financing and other services. The success or failure of a company dep...
Giving credit is one of the core businesses in banking and the importance of credit risk management ...
As the loan is one of the most important products of banking and financial companies who have intere...
Although there are various items that banking systems can sell to make money, their primary source o...
As people's consumption habits change, loan plays a crucial role in our modern society. It provides ...
Credit risk plays a major role in the banking industry business. Banks' main activities involve gran...
The failure or success of the banking industry depends largely on the industrys ability to properly ...
This research contains the discussion the use of machine learning for doing prediction toward a good...
Classification is a powerful tool in Data mining to predict the loan repayment capability of a banki...
Masters Degree. University of KwaZulu-Natal, Durban.Loan lending has become crucial for both individ...
Loans play a crucial role in the financial industry, and predicting the likelihood of a borrower's a...
This master thesis explore the potential of Machine Learning techniques in predicting default of veh...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
This research uses data from Kaggle, which consists of 32,581 rows and 12 columns, to develop a cred...
Credit-lending companies have resorted to the use of Machine Learning algorithms in the recent past ...