TOOLS AND PECULIARITIES OF SOLVING THE CLASSIFICATION PROBLEM IN CREDIT SCORING SYSTEMS

2020 ◽  
Vol 17 (1) ◽  
pp. 51-59
Author(s):  
A.A. Grishin ◽  
◽  
S.P. Stroyev ◽  
Keyword(s):  
Credit Scoring ◽  
Scoring Systems ◽  
Classification Problem

scholarly journals “Social Credit System”: historical prerequisites and doctrinal grounds of the phenomenon

2020 ◽  
pp. 72-88
Author(s):  
Roman Z. Rouvinsky ◽  
Alexey A. Tarasov
Keyword(s):  
Social Control ◽  
Credit Scoring ◽  
Social Institutions ◽  
Scoring Systems ◽  
Credit System ◽  
Russian Science ◽  
Republic Of China ◽  
Social Credit ◽  
History Of ◽  
New Perspective

This article is dedicated to identification and examination of doctrinal grounds and historical prerequisites of the" Social Credit System (trustworthiness)” – a project introduced in the People’s Republic of China in the early 2000s, and currently being “exported” from People’s Republic of China to other countries. In the course of this research, the author analyzed the specific Chinese sources and prerequisites for the creation of modern social rating and control system, as well as non-national sources mostly attributed to the history of Western European political legal thought and Western social institutions. Viewing "Social Credit System" as a technique for exercising social control and oversight, the authors discover its origins in J. Bentham’s project" Panopticon ", Taylor’s philosophy of management, Confucian and legalistic traditions of Imperial China, ideas and institutions of the era of Chinese cultural revolution, as well as U.S. credit scoring systems. This article is the first within Russian science to study the historical and doctrinal prerequisites of China’s "Social Credit System”, taking into account the works of foreign scholars dedicated to the history of its establishment.  A new perspective is given on the Confucian ideas the ideas of Fajia (Legalism) School, which are interpreted as complementary sources of the modern system of social control developed in PRC. The authors believe that China’s “Social Credit System” and the related techniques of control represent a so-called “bridge” that connects “Western” history of the development of social institutions with typically “Eastern” political and sociocultural tradition. In conclusion, attention is turned to the positive aspects, as well as “shadow” side of implementation of the mechanism of “Social Credit System”, “reverse” of this process and all accompanying problems thereof.

Survival Analysis and ROC Analysis in Analyzing Credit Risks

2018 ◽  
pp. 110-133
Author(s):  
Nan Hu ◽  
Haojie Cheng
Keyword(s):  
Competing Risks ◽  
Scoring System ◽  
Roc Analysis ◽  
Credit Scoring ◽  
Scoring Systems ◽  
Time Varying ◽  
Statistical Tool ◽  
Cross Sectional ◽  
Credit Risks ◽  
Over Time

As the aim of large banks has been changing to select customers of highest benefits, it is important for banks to know not only if but also when a customer will default. Survival analyses have been used to estimate over time risk of default or early payoff, two major risks for banks. The major benefit of this method is that it can easily handle censoring and competing risks. An ROC curve, as a statistical tool, was applied to evaluate credit scoring systems. Traditional ROC analyses allow banks to evaluate if a credit-scoring system can correctly classify customers based on their cross-sectional default status, but will fail when assessing a credit-scoring system at a series of future time points, especially when there are censorings or competing risks. The time-dependent ROC analysis was introduced by Hu and Zhou to evaluate credit-scoring systems in a time-varying fashion and it allows us to assess credit scoring systems for predicting default by any time within study periods.

scholarly journals A COMPARISON OF DATA MINING TECHNIQUES FOR CREDIT SCORING IN BANKING: A MANAGERIAL PERSPECTIVE

2009 ◽  
Vol 10 (3) ◽  
pp. 233-240 ◽  
Author(s):  
Huseyin Ince ◽  
Bora Aktan
Keyword(s):  
Neural Networks ◽  
Predictive Accuracy ◽  
Credit Scoring ◽  
Experimental Studies ◽  
Regression Trees ◽  
Scoring Systems ◽  
Classification And Regression Trees ◽  
Insurance Companies ◽  
Real World Data ◽  
Classification And Regression

Credit scoring is a very important task for lenders to evaluate the loan applications they receive from consumers as well as for insurance companies, which use scoring systems today to evaluate new policyholders and the risks these prospective customers might present to the insurer. Credit scoring systems are used to model the potential risk of loan applications, which have the advantage of being able to handle a large volume of credit applications quickly with minimal labour, thus reducing operating costs, and they may be an effective substitute for the use of judgment among inexperienced loan officers, thus helping to control bad debt losses. This study explores the performance of credit scoring models using traditional and artificial intelligence approaches: discriminant analysis, logistic regression, neural networks and classification and regression trees. Experimental studies using real world data sets have demonstrated that the classification and regression trees and neural networks outperform the traditional credit scoring models in terms of predictive accuracy and type II errors.

CREDIT SCORING USING MULTI-KERNEL SUPPORT VECTOR MACHINE AND CHAOS PARTICLE SWARM OPTIMIZATION

2012 ◽  
Vol 11 (03) ◽  
pp. 1250019 ◽  
Author(s):  
YUN LING ◽  
QIUYAN CAO ◽  
HUA ZHANG
Keyword(s):  
Particle Swarm Optimization ◽  
Kernel Function ◽  
Credit Scoring ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Classification Problem ◽  
Classification Performance ◽  
Kernel Functions ◽  
Support Vector ◽  
Swarm Optimization

Consumer credit scoring is considered as a crucial issue in the credit industry. SVM has been successfully utilized for classification in many areas including credit scoring. Kernel function is vital when applying SVM to classification problem for enhancing the prediction performance. Currently, most of kernel functions used in SVM are single kernel functions such as the radial basis function (RBF) which has been widely used. On the basis of the existing kernel functions, this paper proposes a multi-kernel function to improve the learning and generalization ability of SVM by integrating several single kernel functions. Chaos particle swarm optimization (CPSO) which is a kind of improved PSO algorithm is utilized to optimize parameters and to select features simultaneously. Two UCI credit data sets are used as the experimental data to evaluate the classification performance of the proposed method.

scholarly journals A Hybrid Meta-Learner Technique for Credit Scoring of Banks’ Customers

2017 ◽  
Vol 7 (5) ◽  
pp. 2073-2082 ◽  
Author(s):  
A. G. Armaki ◽  
M. F. Fallah ◽  
M. Alborzi ◽  
A. Mohammadzadeh
Keyword(s):  
Machine Learning ◽  
Hybrid Model ◽  
Credit Scoring ◽  
Clustering Algorithms ◽  
Real Life ◽  
Ensemble Methods ◽  
Scoring Systems ◽  
Error Rates ◽  
Machine Learning Algorithms ◽  
Machine Learning Techniques

Financial institutions are exposed to credit risk due to issuance of consumer loans. Thus, developing reliable credit scoring systems is very crucial for them. Since, machine learning techniques have demonstrated their applicability and merit, they have been extensively used in credit scoring literature. Recent studies concentrating on hybrid models through merging various machine learning algorithms have revealed compelling results. There are two types of hybridization methods namely traditional and ensemble methods. This study combines both of them and comes up with a hybrid meta-learner model. The structure of the model is based on the traditional hybrid model of ‘classification + clustering’ in which the stacking ensemble method is employed in the classification part. Moreover, this paper compares several versions of the proposed hybrid model by using various combinations of classification and clustering algorithms. Hence, it helps us to identify which hybrid model can achieve the best performance for credit scoring purposes. Using four real-life credit datasets, the experimental results show that the model of (KNN-NN-SVMPSO)-(DL)-(DBSCAN) delivers the highest prediction accuracy and the lowest error rates.

You Are What You Pay – Personal Profiling with Alternative Payment Data and the Data Protection Law

2020 ◽  
Vol 89 (4) ◽  
pp. 73-88
Author(s):  
Pauline Affeldt ◽  
Ulrich Krüger
Keyword(s):  
Big Data ◽  
Financial Services ◽  
Service Industry ◽  
Credit Scoring ◽  
Scoring Systems ◽  
Global Trend ◽  
Informationelle Selbstbestimmung ◽  
Economic Developments ◽  
Customer Profiles ◽  
Scoring Systeme

Summary: The global trend toward cashless payment started well before the corona pandemic. Along with it, investors in the data-driven tech industry are inspired by the promise of targeted behavioral scoring based on big data. It seems economically tempting to combine these two trends by using all data generated by the payment services to create personal profiles. However, this business model conflicts with the individual’s right of informational self-determination and raises questions regarding inaccuracies, discrimination, and the non-transparency of the algorithms underlying these profiles. Our article provides a short overview over the recent economic developments in the financial service industry and a legal assessment in light of the GDPR. Not everything that is feasible with big data scoring using alternative payment data is legally allowed in Europe. Nevertheless, traditional banks could have the opportunity to improve their internal credit scoring systems and use individual customer profiles to further market their financial services. Zusammenfassung: Nicht erst seit der Corona Pandemie gibt es weltweit den Trend zum bargeldlosen Zahlungsverkehr. Zudem beflügelt die Vorstellung eines zielgenauen Behavioral (Big Data) Scoring die Fantasien von Investoren in der Datentechnologiebranche. Es scheint ökonomisch verführerisch, beide Trends zusammenführen, wenn man alle Daten aus dem Zahlungsverkehr für ein persönliches Profil auswerten würde. Dieses Geschäftsmodell liegt jedoch mit dem Recht des Einzelnen auf informationelle Selbstbestimmung im Konflikt und wirft Fragen auf im Hinblick auf Ungenauigkeit, Diskriminierung und Intransparenz. Unser Artikel gibt einen Überblick über die ökonomische Entwicklung des Sektors und eine rechtliche Bewertung insbesondere aus Sicht der europäischen Datenschutz-Grundverordnung. Nicht alles was im Big Data Scoring mit alternativen Zahlungsdaten möglich sein könnte, ist in Europa auch rechtlich zulässig. Vor allem für die „klassischen“ Banken könnte sich gleichwohl eine Möglichkeit eröffnen ihre internen Credit Scoring Systeme zu verbessern und mit angepasst-individuellen Kundenprofilen weitere ihrer Finanzdienstleistungen zu vertreiben.

ISSUES IN THE DESIGN AND IMPLEMENTATION OF CREDIT SCORING SYSTEMS

1980 ◽  
Vol 15 (4) ◽  
pp. 17-17 ◽  
Author(s):  
Gary G. Chandler ◽  
John Y. Coffman
Keyword(s):  
Credit Scoring ◽  
Scoring Systems ◽  
Design And Implementation

scholarly journals An Explanation Framework for Interpretable Credit Scoring

2021 ◽  
Vol 12 (1) ◽  
pp. 19-38
Author(s):  
Lara Marie Demajo ◽  
Vince Vella ◽  
Alexiei Dingli
Keyword(s):  
Credit Scoring ◽  
Imbalanced Data ◽  
Scoring Systems ◽  
Gradient Boosting ◽  
Home Equity ◽  
General Data Protection Regulation ◽  
Box Models ◽  
Extreme Gradient Boosting ◽  
Feature Based ◽  
The Right

With the recent boosted enthusiasm in Artificial Intelligence (AI) and Financial Technology (FinTech), applications such as credit scoring have gained substantial academic interest. However, despite the evergrowing achievements, the biggest obstacle in most AI systems is their lack of interpretability. This deficiency of transparency limits their application in different domains including credit scoring. Credit scoring systems help financial experts make better decisions regarding whether or not to accept a loan application so that loans with a high probability of default are not accepted. Apart from the noisy and highly imbalanced data challenges faced by such credit scoring models, recent regulations such as the `right to explanation' introduced by the General Data Protection Regulation (GDPR) and the Equal Credit Opportunity Act (ECOA) have added the need for model interpretability to ensure that algorithmic decisions are understandable and coherent. A recently introduced concept is eXplainable AI (XAI), which focuses on making black-box models more interpretable. In this work, we present a credit scoring model that is both accurate and interpretable. For classification, state-of-the-art performance on the Home Equity Line of Credit (HELOC) and Lending Club (LC) Datasets is achieved using the Extreme Gradient Boosting (XGBoost) model. The model is then further enhanced with a 360-degree explanation framework, which provides different explanations (i.e. global, local feature-based and local instance- based) that are required by different people in different situations. Evaluation through the use of functionally-grounded, application-grounded and human-grounded analysis shows that the explanations provided are simple and consistent as well as correct, effective, easy to understand, sufficiently detailed and trustworthy.

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