Implementation of machine learning in finance for classification of binary rating of finance credit card companies using MATLAB..!

Implementation of machine learning in finance for classification of binary rating of finance credit card companies using MATLAB..!

MATLABSolutions demonstrate how to Implementation of machine learning in finance for classification of binary rating of finance credit card companies using MATLAB.After the sub-prime mortgage crisis of 2007 and global crisis of 2008, credit risk analysis hasbecomes more important than ever before. Basel committee on banking supervision releasedBasel II, which required supervised financial institutions to use internal rating to measure their credit risk exposure. Those actions resulted in banks enhancing methods of credit risk analysis.

Credit risk is the loss to lenders due to borrowers defaulting on their credit obligations.Making decisions about the approval of credit and the interest rate relies on the assessment of credit risk for lenders. There are credit-scoring systems that are designed to enhance lenders’abilities in evaluating creditworthiness of customers in the process of credit risk analysis.

Abstract:

After the sub-prime mortgage crisis of 2007 and global crisis of 2008, credit risk analysis has become more important than ever before. This paper conducts the credit risk analysis and compares classification performances among different algorithms (logistic regression, support vector machine, decision tree, multilayer perception, probabilistic neural network, Deep Learning) by using a large peer-to-peer lending dataset composed of a million observations. The findings show that Support vector machine (SVM) provides the most accurate performance, followed by decision tree, logistic regression, multilayer perceptron neural network, probabilistic neural network and deep learning. The main contributions of this paper is the reapplication of machine learning techniques to an alternate dataset composed of significantly larger number of observations with deviating pattern from traditional bank loans. The findings from SVM and Decision tree are consistent with the previous literature. The results from logistic regression and MLP indicate that they are identical based on p2p dataset, which makes a contribution to the debate whether MLP out performs logistic regression. For PNN it is difficult to say if it properly accounts for the data imbalance due to the low performance of the model compared to the others. Deep learning performance is in contrast to previous work as it is the worst performing model comparing with other investigated techniques. This is potentially due to the simple approach to deep learning that this paper adopted and opens up the topic for future research.

Decision trees

Decision trees are statistical data mining technique that express independent attributes and a dependent attributes logically AND in a tree shaped structure. Classification rules, extracted from decision trees, are IF-THEN expressions and all the tests have to succeed if each rule is to be generated. Decision tree usually separates the complex problem into many simple ones and resolves the sub problems through repeatedly using .Decision trees are predictive decision support tools that create mapping from observations to possible consequences. There are number of popular classifiers construct decision trees to generate class models.

Decision tree, also called as classification trees, is a fast and easy type of classifier to understand and interpret. An important feature of this technique is that data has few variations, which could be used to learn, produce important differences in the model (Tsymbal, Pechenizkiy, & Cunninghan, 2005). A decision tree method divide a large amount of observations into several smaller homogeneous group based on a set of rules and particular target variable.

Davis et al (1992) apply decision tree and a multilayer perceptron neural network to conduct a research related to credit card scoring. Based on a single data partition and neural network, it concludes that a comparable level of accuracy is obtained for decision tree model and multilayer perceptron neural network (Davis, Edelman, & Gammerman, 1992). En empirical evidence find that no model outperform the other when logistic regression model, decision tree and credit scorecard mode are compared. The classification error rates of logistic regression model, decision tree and credit scorecard model are 28.8%, 28.1% and 27.9% respectively.

Machine learning

Machine learning in finance has become more prominent recently due to the availability of vast amounts of data and more affordable computing power.Machine learning in finance is reshaping the financial services industry like never before.Leading banks and financial services companies are deploying AI technology, including machine learning (ML), to streamline their processes, optimise portfolios, decrease risk and underwrite loans amongst other things.Here in this article, we will explore some important ways machine learning is transforming the financial services sector and examples of real applications of machine learning in finance.To answer this question and understand the role of machine learning in finance, we must first understand why machine learning is suitable for finance.