Add to ORKGAbstract:- Supervised learning plays a significant role in predicting the behavior of new data, based on the rules, which are extracted keeping in view the behavior of existing data in the database. This paper is about algorithmic analysis of supervised learning for transactional data. Our main idea is to apply different classification algorithms on a preprocessed financial data set in order to evaluate that which type of classification algorithm under what sort of data model selection and with what combination of mining attributes is best suited for a transactional and frequently occurring data. In this way the algorithm with highest accuracy can be used to predict the credit rating of a client, based on his past transactions. It can be ve...
The knowledge of the nature of the master data managed by a trading platform and the classification ...
We describe a case study in data mining for personal loan evaluation, performed at the ABN AMRO bank...
Credit scoring is one of the most important dimensions of the decision-making process for the loan i...
Machine learning methods penetrate to applications in the analysis of financial data, particularly t...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
In our master thesis, we compare ten classification algorithms for credit scor- ing. Their predictio...
The article presents the basic techniques of data mining implemented in typical commercial software....
Data mining is a sum of process to find anomalies, patterns, correlations which can assist banks to ...
Since incorrect decisions can have detrimental effects on financial institutions, the possibility fo...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodolog...
There is an increasing need for credit decision making systems that can dynamically analyze historic...
Modern methods for classification analysis involve processes for “learning” to correctly assign elem...
This master's thesis is divided into three parts. In the first part I described P2P lending, its cha...
The knowledge of the nature of the master data managed by a trading platform and the classification ...
We describe a case study in data mining for personal loan evaluation, performed at the ABN AMRO bank...
Credit scoring is one of the most important dimensions of the decision-making process for the loan i...
Machine learning methods penetrate to applications in the analysis of financial data, particularly t...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
One of the major challenges facing the retail finance market including banks is the issue of credit ...
In our master thesis, we compare ten classification algorithms for credit scor- ing. Their predictio...
The article presents the basic techniques of data mining implemented in typical commercial software....
Data mining is a sum of process to find anomalies, patterns, correlations which can assist banks to ...
Since incorrect decisions can have detrimental effects on financial institutions, the possibility fo...
For many years lenders have been using traditional statistical techniques such as logistic regressio...
This chapter describes Data Mining in finance by discussing financial tasks, specifics of methodolog...
There is an increasing need for credit decision making systems that can dynamically analyze historic...
Modern methods for classification analysis involve processes for “learning” to correctly assign elem...
This master's thesis is divided into three parts. In the first part I described P2P lending, its cha...
The knowledge of the nature of the master data managed by a trading platform and the classification ...
We describe a case study in data mining for personal loan evaluation, performed at the ABN AMRO bank...
Credit scoring is one of the most important dimensions of the decision-making process for the loan i...