Intelligent Detection of Bad Credit Card Accounts

Huang, Yo-Ping; Sandnes, Frode Eika; Chang, Tsun-Wei; Lu, Chun-Chieh

doi:10.5772/6280

Cited by 5 publications

(7 citation statements)

References 11 publications

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“…Using fuzzy legal and linguistic variables in fuzzy system decision making is possible when data is incomplete and uncertain. Each fuzzy system contains four major components (Huang & Chang, 2007):…”

Section: Automatic Learner's Personality Identifier System (Alpis)mentioning

confidence: 99%

E-learners’ personality identifying using their network behaviors

Ghorbani

Montazer

2015

Computers in Human Behavior

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Section: Automatic Learner's Personality Identifier System (Alpis)mentioning

confidence: 99%

E-learners’ personality identifying using their network behaviors

Ghorbani

Montazer

2015

Computers in Human Behavior

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“…In [9] neuro-fuzzy system is presented to find invalid accounts and to predict them which has facilitated these accounts detection with a relatively high precision. In this study the history of the committed crime in system and also the information related to the people who have problems redeeming their mortgage are used.…”

Section: Related Workmentioning

confidence: 99%

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

Maryam¹,

Ali²

2019

AJARR

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World economy has encountered money laundering phenomenon and its destructive effects on economy of countries over the last decades. Money laundry detection is one of the fields in which data mining tools can be very helpful in detecting it. Nowadays, recognizing credible customers to present banking facilities to them is of high importance. On the other hand, by increasing cheating in banking, detecting the fraud customers is also important. In this study, the decision tree was being trained by providing a fuzzy decision tree and users’ behavioral vectors. The output of fuzzy tree can signify users’ risking behaviors. Some features of customers’ accounts need to be extracted in order to identify the people with high risks. For instance, the variance of money transaction and money transfer can be done. In another part of the study, RFM (Recency, Frequency, Monetary) features and MLP (Multi-Layer Perception) classifications were used to identify the loyal customers. The three-fold features of refreshment, frequency, and shopping amount was completely discussed for every customer, whereby the customers’ scores were established as well. This classification aimed to categorize the credible users of neural networks. The results of current research indicate that the represented techniques possess high precision values, in comparison with previous techniques.

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“…Of course, if we add the undeclared statistics from financial institutions to these figures, the rate of this growth will be further [3]. Thus, nowadays online fraud has become more complicated in Internet banking and the security and trust has been more seriously addressed in e-banking [6]. Due to European Union Agency for Fundamental Rights [7] a one per four Europeans (24%) feels a huge amount of worries regards to misuse of their online bank account or payment card details by fraudsters or criminals.…”

Section: Introductionmentioning

confidence: 99%

A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining

et al. 2021

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As a result of changes in approach from traditional to virtual banking system, security in data exchange has become more important; thus, it seems essentially necessary to present a pattern based on smart models in order to reduce fraud in this field. A new algorithm has been provided in this article to improve security and to specify the limits of giving special services to Internet banking users in order to pave appropriate ground for virtual banking. In addition to identifying behavioral models of customers, this algorithm compares the behaviors of any customer with this model and finally computes the rate of trust in customer’s behavior. The hybrid data-mining and knowledge based structure has been adapted in this algorithm according to fuzzy systems. In this research, qualitative data was gathered from interviews with banking experts, analyzed by Expert Choice to identify the most important variables of customer behavior analysis, and to analyze customer behavior and customer bank Internet transaction data for a period of one year by MATLAB and Clementine. The results of this survey indicate that the potential of the given structure to recognize the rate of trust in Internet bank user’s behavior might be at reasonable level for experts in this area.

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Intelligent Detection of Bad Credit Card Accounts

Cited by 5 publications

References 11 publications

E-learners’ personality identifying using their network behaviors

E-learners’ personality identifying using their network behaviors

Evaluation of Destructive e-Banking Users’ Loyalty by Applying RFM

A New Model to Identify the Reliability and Trust of Internet Banking Users Using Fuzzy Theory and Data-Mining

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