Credit Scoring Analysis: Case Study of Using Weka

2015

The explosive growth of data in banking sector is common phenomena. It is due to early adaptation of information system by Banks. This vast volume of historical data related to financial position of individuals and organizations compel banks to evaluate credit worthiness of clients to offers new services. Credit scoring can be defined as a technique that facilitates lenders in deciding to grant or reject credit to consumers. A credit score is a product of advanced analytical models that catch a snapshot of the consumer credit history and translate it into a numeric number that signify the amount of risks that will be generated in a specific deal by the consumer. Automated Credit scoring mechanism has replaced onerous, error-prone labour-intensive manual reviews that were less transparent and lacks statistical-soundness in almost all financial organizations. The credit scoring functionality is a type of classification problem for the new customer. There are numerous data classificati...

International Journal of Advanced Trends in Computer Science and Engineering, 2020

The rapid expansion of credit scoring technologies is increased today. Credit scoring will be considered as the significant element in the financial industries. It plays an important role in modern affairs such as credit customer selection, risk measurement, post-loan and after-loan supervision, comprehensive performance evaluation etc. Credit scoring has been recognized as a binary classification technique distinguishing applicants into two classes: good credit and bad credit, based on characteristics such as gender, age, occupation, and salary. These determine the applicability of loans for applicants. There are two main stream classification techniques, statistical techniques and machine learning techniques. Linear discriminant analysis and logistic regression are the two most commonly used statistical techniques in credit scoring. Machine learning techniques include K-nearest neighbor, support vector machine, decision tree and neural network. Use different best algorithms for classify the credit scoring data sets. Here uses four algorithms for the classification of credit scoring data sets and then the accuracy of different algorithms on the data sets will be obtained.

Today`s financial transactions have been increased through banks and financial institutions. Therefore, credit scoring is a critical task to forecast the customers’ credit. We have created 9 different models for the credit scoring by combining three methods of feature selection and three decision tree algorithms. The models are implemented on three datasets and then the accuracy of the models is compared. The two datasets are chosen from the UCI (Australian dataset, German dataset) and a given dataset is considered a Car Leasing Company in Iran. Results show that using feature selection methods with decision tree algorithms (hybrid models) make more accurate models than models without feature selection.

Neural Computing and Applications, 2019

Among the numerous alternatives used in the world of risk balance, it highlights the provision of guarantees in the formalization of credit agreements. The objective of this paper is to compare the achievement of fuzzy sets with that of artificial neural network-based decision trees on credit scoring to predict the recovered value using a sample of 1890 borrowers. Comparing with fuzzy logic, the decision analytic approach can more easily present the outcomes of the analysis. On the other hand, fuzzy logic makes some implicit assumptions that may make it even harder for credit-grantors to follow the logical decision-making process. This paper leads an initial study of collateral as a variable in the calculation of the credit scoring. The study concludes that the two models make modelling of uncertainty in the credit scoring process possible. Although more difficult to implement, fuzzy logic is more accurate for modelling the uncertainty. However, the decision tree model is more favourable to the presentation of the problem.

Credit is the one of the biggest contributor to the bank profit, other than products and services, but credit can also become the biggest loss contributor to the bank if credit quality, which is called as collectability not maintained properly. Good credit growth and expansion is needed to be in line with credit quality improvement so the maximum profit could be generated. This research seek to answer these question, how quantitative and qualitative data inside system that use decision tree can optimalized credit quality determination on PT. XYZ. Algorithm that is used in the Decision Tree method is Iterative Dichotomizer Three (ID3). Some of the advantages of ID3 Algorithm which are it could create understandable predicition rules, more faster, and only need some attribute test until all data is classified.

2013 IEEE 14th International Conference on Information Reuse & Integration (IRI), 2013

Network. The purpose of this paper is to conduct a comparative study on the accuracy of classification models and reduce the credit risk. In this paper, we use data mining of enterprise software to construct four classification models, namely, decision tree, logistic regression, neural network and support vector machine, for credit scoring in banking. We conduct a systematic comparison and analysis on the accuracy of 17 classification models for credit scoring in banking. The contribution of this paper is that we use different classification methods to construct classification models and compare classification models accuracy, and the evidence demonstrates that the support vector machine models have higher accuracy rates and therefore outperform past classification methods in the context of credit scoring in banking.

International Journal of Computer Applications, 2017

Lenders such as banks and credit card companies while reviewing a client"s request for loan use credit scores. Credit scores help measure the creditworthiness of the client using a numerical score. Now it has been found out that the problem can be optimized by using various statistical models. In this study a wide range of statistical methods in machine learning have been applied, though the datasets available to the public is limited due to confidentiality concerns. Problems particular to the context of credit scoring are examined and the statistical methods are reviewed.

Journal of Autonomous Intelligence

In order to evaluate a person’s or a company’s creditworthiness, financial institutions must use credit scoring. Traditional credit scoring algorithms frequently rely on manual and rule-based methods, which can be tedious and inaccurate. Recent developments in artificial intelligence (AI) technology have opened up possibilities for creating more reliable and effective credit rating systems. The data are pre-processed, including scaling using the 0–1 normalization method and resolving missing values by imputation. Information gain (IG), gain ratio (GR), and chi-square are three feature selection methodologies covered in the study. While GR normalizes IG by dividing it by the total entropy of the feature, IG quantifies the reduction in total entropy by adding a new feature. Based on chi-squared statistics, the most vital traits are determined using chi-square. This research employs different ML models to develop a hybrid model for credit score prediction. The ML algorithms support vec...

Credit scoring can be defined as a technique that helps credit providers decide Whether to grant credit to consumers or customers. Its increasing importance can be seen from the growing popularity and application of credit scoring in consumer credit. There are advantages not only to construct effective credit scoring models to help improve the bottom-line of credit providers, but also to combine models to yield a better performing combined model. In this paper we described 1) The use of data mining techniques to construct credit scoring models. 2)The combination of credit scoring models to give a superior final model.

Zimbabwe Journal of Science and Technology, 2018

Credit risk mitigation is an area of renewed interest due to the 2007-2008 financial crises and thus masses of data are collected by the financial institutions. This has left the risk analysts with a daunting task of adequately determining the credit worthiness of an individual. In the search for highly efficient credit scoring models, financial institutions can adopt sophisticated machine learning techniques. We employ the AUROC approach to make a comparative analysis of machine learning methods of classification by performing 10-fold cross validation for model selection on the German Credit data set from the UCI database. The results show that Lasso regression provides the best estimation for default with an AUROC of 0.8048 followed by the Random Forest model with 0.7869 AUROC. The widely used logit model performed better than the Support Vector Machine (Linear) with 0.7678 and 0.7581 AUROC respectively. Moreover, by the Kolmogorov-Smirnov test, we proved that the other machine learning techniques outperform the widely used logit model in how well the model is able to classify "good" class from "bad" class.