Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring

Laha, Arijit

doi:10.1016/j.aei.2006.12.004

Cited by 53 publications

(28 citation statements)

References 11 publications

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“…However, the use of these methods was not observed until the early 1960s which represented the development period of relevant computer technologies (Capon, 1982). Laha (2007) defined credit scoring as a way of anticipating potential risk belonging to a credit portfolio which can be split into two as application scoring and behavioural scoring. Demographic data of borrowers is used for computing the application score whereas historical payment behaviour of the applicant is used for the behavioural score.…”

Section: Credit Scoringmentioning

confidence: 99%

Strategic Decision Making Of Micro-Lenders: An Innovative Business Model For Risk Management

Alma¹

2017

The European Proceedings of Social and Behavioural Sciences

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Strategic decision-making processes necessitate a significant amount of experience and the consideration of a wide range of factors associated with the decision problem. Determination of applicant credit score and loan decisions are such decisions that subjective judgement of loan officers can produce poor results with substantial losses. This study is triggered as a result of an industrial consultancy project with a partner providing technological solutions for financial services. Anticipated requirement for an epayment system that connects financial lenders, credit applicants and retailers digitally through a system is the main motivation of the study. Another focus of the study is on providing immediate loan access to retail customers which has necessitated automated processes and a decision-support system to assist the analysis of loan applications. The proposed system automatically evaluates a credit application as soon as the relevant information is inputted to the system at the end of the shopping process. The decision-making process should be supported with appropriate systems performing computerized credit assessment processes for quick and accurate decisions. The main complementary tools for the proposed system include a fraud detection module and credit risk assessment module which runs credit scoring algorithms. The proposed system will make loan or credit line decisions faster by eliminating paper work, easier by eliminating bureaucracy, and more accurate by newly integrated algorithms and approaches.

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Section: Credit Scoringmentioning

confidence: 99%

Strategic Decision Making Of Micro-Lenders: An Innovative Business Model For Risk Management

Alma¹

2017

The European Proceedings of Social and Behavioural Sciences

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“…This real world dataset, which classifies credit applicants described by a set of attributes as good or bad credit risks, has been successfully used for credit scoring and evaluation systems in many previous works [11][12][13][14][15][16][17][18][19][20][21].…”

Section: Related Workmentioning

confidence: 99%

Modeling consumer loan default prediction using ensemble neural networks

Hassan

Abraham

2013

2013 International Conference on Computing, Electrical and Electronic Engineering (Icceee)

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Abstract-In this paper, a loan default prediction model is constricted using three different training algorithms, to train a supervised two-layer feed-forward network to produce the prediction model. But first, two attribute filtering functions were used, resulting in two data sets with reduced attributes and the original data-set. Back propagation based learning algorithms was used for training the network. The neural networks are trained using real world credit application cases from a German bank datasets which has 1000 cases; each case with 24 numerical attributes; upon, which the decision is based. The aim of this paper was to compare between the resulting models produced from using different training algorithms, scaled conjugate gradient backpropagation, Levenberg-Marquardt algorithm, One-step secant backpropagation (SCG, LM and OSS) and an ensemble of SCG, LM and OSS. Empirical results indicate that training algorithms improve the design of a loan default prediction model and ensemble model works better than the individual models.Index Terms -credit risk, loan default, neural network, scaled conjugate gradient backpropagation, Levenberg-Marquardt algorithm and One-step secant backpropagation.

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“…Credit scoring tasks can be divided into two distinct types. The first type is application scoring, where the task is to classify credit applicants into ''good'' and ''bad'' risk groups [1]. The second type of tasks deal with existing customers and along with other information, payment history information is also used here.…”

Section: Introductionmentioning

confidence: 99%

“…The second type of tasks deal with existing customers and along with other information, payment history information is also used here. This is distinguished from the first type because this takes into account the customer's payment pattern on the loan and the task is called behavioral scoring [1].Classification and association-rule discovery are two of the most important tasks addressed in the data mining literature. In recent years, extensive research has been carried out to integrate both approaches.…”

Section: Introductionmentioning

confidence: 99%

“…For example in a paper, The Ranked Multilabel Rule (RMR) algorithm is introduced as a new associative classification technique, which is generating the rules with multiple labels [3]. There is another study which presents a new fuzzy data-mining algorithm to extract both fuzzy association rules and membership functions by means of a genetic learning of the membership functions and a basic method for mining fuzzy association rules [4].A method of building credit-scoring models using fuzzy rule based classifiers has been suggested in another research which the rule base is learned from the training data using a SOM based method and Then design a contextual classifier by incorporating the fuzzy k-nn rule is with it for better classification [1]. In a recent application in the field of credit scoring, a decision support system is developed for the banks to determine the credibility of manufacturing firms in Turkey which is based on the financial ratios and fuzzy TOPSIS approach [5].Another study applied neuro-fuzzy models to analyze consumer loan applications and compared the advantages of neuro-fuzzy systems over traditional statistical techniques in credit-risk evaluation [6].…”

Section: Introductionmentioning

confidence: 99%

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Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

Nosratabadi¹,

Nadali²,

Pourdarab³

2012

IJMLC

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Abstract-Credit assessment is a very typical classification problem in Data Mining. A type of classification technique that has attracted an increasing number of attempts in recent years is finding classification rules based on association rule mining techniques. This paper aims to contribute to this kind of research by classifying the bank's customers via association rules with the use of the APRIORI algorithm and CRISP-DM methodology and considering the Experts' opinions to filter the obtained rules and define the Membership functions for the considered criteria, finally a Fuzzy Expert System is designed based on the selected rules from association rules to specify the Credit Degree of banks' customers. The presented steps have been studied in an Iranian Bank as empirical study.

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Building contextual classifiers by integrating fuzzy rule based classification technique and k-nn method for credit scoring

Cited by 53 publications

References 11 publications

Strategic Decision Making Of Micro-Lenders: An Innovative Business Model For Risk Management

Strategic Decision Making Of Micro-Lenders: An Innovative Business Model For Risk Management

Modeling consumer loan default prediction using ensemble neural networks

Credit Assessment of Bank Customers by a Fuzzy Expert System Based on Rules Extracted from Association Rules

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