Literature Study –Data Mining Techniques on Detecting Fradulent Activities in Credit Card

2017 ◽  
Vol 6 (10) ◽  
pp. 60
Author(s):  
S. K. Saravanan ◽  
G. N. K. Suresh Babu
Keyword(s):  
Data Mining ◽  
Credit Card ◽  
Data Transfer ◽  
Study Data ◽  
Time Requirement ◽  
Literature Study ◽  
Data Mining Techniques ◽  
Online Transactions ◽  
Secured Data ◽  
Credit Card Usage

In contemporary days the more secured data transfer occurs almost through internet. At same duration the risk also augments in secure data transfer. Having the rise and also light progressiveness in e – commerce, the usage of credit card (CC) online transactions has been also dramatically augmenting. The CC (credit card) usage for a safety balance transfer has been a time requirement. Credit-card fraud finding is the most significant thing like fraudsters that are augmenting every day. The intention of this survey has been assaying regarding the issues associated with credit card deception behavior utilizing data-mining methodologies. Data mining has been a clear procedure which takes data like input and also proffers throughput in the models forms or patterns forms. This investigation is very beneficial for any credit card supplier for choosing a suitable solution for their issue and for the researchers for having a comprehensive assessment of the literature in this field.

A Data Mining Approach for Risk Assessment in Car Insurance

2014 ◽  
Vol 5 (3) ◽  
pp. 11-28
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Milijana Novović-Burić
Keyword(s):  
Data Mining ◽  
Risk Assessment ◽  
Risk Premium ◽  
Study Data ◽  
Functional Dependencies ◽  
Standard Methods ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
The One ◽  
Car Insurance

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

A Data Mining Approach for Risk Assessment in Car Insurance

Big Data ◽  
2016 ◽  
pp. 2028-2046
Author(s):  
Ljiljana Kašćelan ◽  
Vladimir Kašćelan ◽  
Milijana Novović-Burić
Keyword(s):  
Data Mining ◽  
Risk Assessment ◽  
Risk Premium ◽  
Study Data ◽  
Functional Dependencies ◽  
Standard Methods ◽  
Data Mining Techniques ◽  
Data Mining Approach ◽  
The One ◽  
Car Insurance

This paper has proposed a data mining approach for risk assessment in car insurance. Standard methods imply classification of policies to great number of tariff classes and assessment of risk on basis of them. With application of data mining techniques, it is possible to get functional dependencies between the level of risk and risk factors as well as better results in predictions. On the case study data it has been proved that data mining techniques can, with better accuracy than the standard methods, predict claim sizes and occurrence of claims, and this represents the basis for calculation of net risk premium and risk classification. This paper, also, discusses advantages of data mining methods compared to standard methods for risk assessment in car insurance, as well as the specificities of the obtained results due to small insurance market, such is the one in Montenegro.

scholarly journals Prediction on Deposit Subscription of Customer based on Bank Telemarketing using Decision Tree with Entropy Comparison

2020 ◽  
Vol 4 (2) ◽  
pp. 57-66
Author(s):  
Ardytha Luthfiarta ◽  
Junta Zeniarja ◽  
Edi Faisal ◽  
Wibowo Wicaksono
Keyword(s):  
Data Mining ◽  
Decision Making ◽  
Decision Tree ◽  
Credit Card ◽  
Banking System ◽  
Decision Making Process ◽  
Data Mining Techniques ◽  
Related Information ◽  
Customer Information ◽  
Tree Algorithms

Banking system collect enormous amounts of data every day. This data can be in the form of customer information,  transaction  details,  risk profiles,   credit   card   details,   limits   and   collateral    details, compliance  Anti Money Laundering (AML) related information, trade  finance  data,  SWIFT  and  telex  messages. In addition,  Thousands  of decision  are  made in Banking system. For example, banks everyday creates credit decisions,  relationship  start  up,  investment   decisions, AML  and  Illegal  financing  related decision.  To create this decision, comprehensive review on various  reports  and drills  down  tools  provided  by the banking systems is needed.  However, this is a manual process which  is  error  prone  and  time  consuming  due  to  large volume of transactional  and historical  data available. Hence, automatic knowledge mining is needed to ease the decision making process.  This research focuses on data mining techniques to handle the mentioned problem. The technique will focus on classification method using Decision Tree algorithms.  This research provides an overview of the data mining techniques and   procedures will be performed.   It also provides   an insight   into how these techniques can be used in deposit subscription  in banking system to make a decision making process easier and more productive. Keywords - Telemarketing, bank deposit, decision tree, classification, data mining, entropy.

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