Classifiers consensus system approach for credit scoring

Banks take great care when dealing with customer loans to avoid any improper decisions that can lead to loss of opportunity or financial losses. Regarding this, researchers have developed complex credit scoring models using statistical and artificial intelligence (AI) techniques to help banks and financial institutions to support their financial decisions. Various models, from easy to advanced approaches, have been developed in this domain. However, during the last few years there has been marked attention towards development of ensemble or multiple classifier systems, which have proved their ability to be more accurate than single classifier models. However, among the multiple classifier systems models developed in the literature, there has been little consideration given to: 1) combining classifiers of different algorithms (as most have focused on building classifiers of the same algorithm); or 2) exploring different classifier output combination techniques other than the traditional ones, such as majority voting and weighted average. In this paper, the aim is to present a new combination approach based on classifier consensus to combine multiple classifier systems (MCS) of different classification algorithms. Specifically, six of the main well-known base classifiers in this domain are used, namely, logistic regression (LR), neural networks (NN), support vector machines (SVM), random forests (RF), decision trees (DT) and naïve Bayes (NB). Two benchmark classifiers are considered as a reference point for comparison with the proposed method and the other classifiers. These are used in combination with LR, which is still considered the industry-standard model for credit scoring models, and multivariate adaptive regression splines (MARS), a widely adopted technique in credit scoring studies. The experimental results, analysis and statistical tests demonstrate the ability of the proposed combination method to improve prediction performance against all base classifiers, namely, LR, MARS and seven traditional combination methods, in terms of average accuracy, area under the curve (AUC), the H-measure and Brier score (BS). The model was validated over five real-world credit scoring datasets

An Overview of Big Data Analytics in Banking: Approaches, Challenges and Issues

Banks are harnessing the power of Big Data. They use Big Data and Data Science to drive change towards data and analytics to gain an overall competitive advantage. The Big Data has potential to transform enterprise operations and processes especially in the banking sector, because they have huge amount of transaction data. The goal of this paper is to give an overview of different approaches and challenges that exists in Big Data in banking sector. The work presented here will fulfill the gap of research papers in the last five years, with focus on Big Data in central banks and credit scoring in central banks. For this paper, we have reviewed existing research literature, official reports, surveys and seminars of central banks, all these related directly or indirectly to Big Data in banks

Credit risk prediction in an imbalanced social lending environment

© 2018, the Authors. Credit risk prediction is an effective way of evaluating whether a potential borrower will repay a loan, particularly in peer-to-peer lending where class imbalance problems are prevalent. However, few credit risk prediction models for social lending consider imbalanced data and, further, the best resampling technique to use with imbalanced data is still controversial. In an attempt to address these problems, this paper presents an empirical comparison of various combinations of classifiers and resampling techniques within a novel risk assessment methodology that incorporates imbalanced data. The credit predictions from each combination are evaluated with a G-mean measure to avoid bias towards the majority class, which has not been considered in similar studies. The results reveal that combining random forest and random under-sampling may be an effective strategy for calculating the credit risk associated with loan applicants in social lending markets

Integrating of the enterprise architecture and multicriteria approaches to evaluate the degree of maturity in an organization

La necesidad de alinear las estrategias de la organización con las nuevas tecnologías de la información y comunicaciones ha sido impuesta por el desarrollo alcanzado por las mismas, siendo la Arquitectura Empresarial (AE) uno de los enfoques que posibilita realizar dicha alienación. Para implementar un proyecto de AE, se necesita partir de un diagnóstico que determine el estado actual de la organización para desarrollar, mantener y aplicar este enfoque, a través de un proceso continuo. Para ello se puede utilizar un modelo de madurez, que da la pauta para desarrollar estrategias de mejoras y alcanzar los objetivos previstos. El presente trabajo tiene como objetivo diseñar una tecnología que permita evaluar el grado de madurez de una organización. La propuesta parte de la aplicación de una lista de chequeo que recoge los aspectos fundamentales de las capas que conforman la Arquitectura Empresarial. Las preferencias de los cuadros y trabajadores involucrados son consideradas utilizando un enfoque Multicriterio, en particular, una función Scoring. Como resultado, se obtienen los niveles de madurez de las organizaciones consideradas y las aplicaciones permitieron detectar que las capas de mayores insuficiencias son las de negocio e información.The need to align the strategies of the organization with the new information technologies had been imposed by the development of these last. One of the approaches that makes it possible such alienation is the Enterprise Architecture Approach (AE). To implement an AE project, it is necessary to start with a diagnosis that determines the current state of the organization to develop, apply and maintain this approach, through a continuous process. For this purpose, a maturity model can be used, which allows to draw up development strategies to achieve the planned objectives and identify improvement areas.The present work has as objective to design a technology that allows evaluating the degree of maturity of an organization. The proposal starts from the application of a checklist that includes the fundamental aspects of the layers that make up the Enterprise Architecture. The preferences of the leaders and workers involved are considered using a Multicriteria approach, in particular, a weighted sum function (Scoring). As a result, the levels of maturity of the organizations considered are obtained, besides the applications allowed to detect that the layers of greater insufficiencies are those of business and informationUniversidad Pablo de Olavid

Classification Algorithms in Financial Application: Credit Risk Analysis on Legal Entities

This research aims at analyzing bank credit of legal entity (in non-default, default and temporarily default), for the purpose of assisting the decision made by the analyst of this area. For that, we used Artificial Neural Networks (ANNs), more specifically, the Multilayer Perceptron (MLP) and the Radial Basis Functions (RBF) and, also, the statistical model of Logistic Regression (LR). For the implementation of the ANNs and LR, the softwares MATLAB and SPSS were used, respectively. For the simulations developed 5.432 data with 15 attributes were collected by the experts of the institution bank (called “XYZ”). The results show that the default clients are easily identifiable, but for the nondelinquent clients and for the temporarily defaulters, the techniques had greater difficulty in the discrimination, suggesting that they are no so discriminants. The main contributions of this work are: the analysis of three classes of clients (non-default, default and temporarily default), rather than just two (non-default and default) as is usually done; the coding of variables (attributes) of the company XYZ aiming to maximize the accuracy of the techniques and the use of the one-against all method, little used by the researchers of this research area. This work presents new insights towards research over Credit Risk Assessment showing other possibilities of client classification and codification, allowing different types of studies to take place

Digitalisation and Big Data Mining in Banking

open access articleBanking as a data-intensive subject has been progressing continuously under the promoting influences of the era of big data. Exploring the advanced big data analytic tools like Data Mining (DM) techniques is key for the banking sector, which aims to reveal valuable information from the overwhelming volume of data and achieve better strategic management and customer satisfaction. In order to provide sound direction for the future research and development, a comprehensive and most up to date review of the current research status of DM in banking will be extremely beneficial. Since existing reviews only cover the applications until 2013, this paper aims to fill this research gap and presents the significant progressions and most recent DM implementations in banking post 2013. By collecting and analyzing the trends of research focus, data resources, technological aids, and data analytical tools, this paper contributes to bringing valuable insights with regard to the future developments of both DM and the banking sector along with a comprehensive one stop reference table. Moreover, we identify the key obstacles and present a summary for all interested parties that are facing the challenges of big data

Study of Banking Customers Credit Scoring Indicators Using Artificial Intelligence and Delphi Method

With the importance of lending in the banking industry, it is very important to use the indicators affecting credit to decide on lending. The purpose of the present study is to identify and prioritize the effective features in customer accreditation using the viewpoints of bank experts in Kerman and to compare them with existing indicators in models extracted from Meta-Heuristic and Artificial Intelligence methods. The aim is to find out whether there is a match between the human views that arise from knowledge and experience and the views of artificial intelligence that look at the problem as black-box modeling. Required data were collected by questionnaire method and Quantum Binary particle swarm optimization algorithm and analyzed by Delphi. The results show that the selected indices have 80% overlap between the two methods. Due to the results of research and high accuracy of artificial intelligence techniques, it is suggested that in order to give credit to customers in banks and financial and credit institutions, to consider a higher weight for these indicators

Using fundamental, market and macroeconomic variables to predict financial distress : a study of companies listed on the Johannesburg Stock Exchange

This study presents a three-stage approach in determining financial distress of companies listed on the Johannesburg Stock Exchange. A novel feature of the present study is that it deviates from a binary classification of corporate distress prediction to present a multinomial outcome where the model predicts distressed, depressed and healthy companies. The research results show an improvement in the prediction accuracy rate when fundamental data is combined with market-based data. However, the further addition of macroeconomic indicators does not enhance the prediction accuracy.This manuscript is based on S.W.S.’s PhD thesis, submitted at the University of Pretoria. L.M.B. was the supervisor while J.H.H. and H.P.W. were co-supervisors. (http://hdl.handle.net/2263/60519)https://www.jefjournal.org.zaam2018Financial Managemen