Data mining techniques for analyzing bank customers: A survey

Hasheminejad, Seyed Mohammad Hossein; Khorrami, Mojgan

doi:10.3233/idt-180335

Cited by 5 publications

(1 citation statement)

References 49 publications

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“…Heuristic data mining applications such as clustering, association rule mining, decision trees are commonly used in banking and finance for risk scoring and churn analysis [19,20] Data mining applications provide more accurate results when more data is available for the same constituents [21,22]. Organizations often collaboratively use data mining applications while sharing data with each other.…”

Section: -Results and Discussionmentioning

confidence: 99%

Data Mining Applications in Banking Sector While Preserving Customer Privacy

Doğuç

2022

Emerg Sci J

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In real-life data mining applications, organizations cooperate by using each other’s data on the same data mining task for more accurate results, although they may have different security and privacy concerns. Privacy-preserving data mining (PPDM) practices involve rules and techniques that allow parties to collaborate on data mining applications while keeping their data private. The objective of this paper is to present a number of PPDM protocols and show how PPDM can be used in data mining applications in the banking sector. For this purpose, the paper discusses homomorphic cryptosystems and secure multiparty computing. Supported by experimental analysis, the paper demonstrates that data mining tasks such as clustering and Bayesian networks (association rules) that are commonly used in the banking sector can be efficiently and securely performed. This is the first study that combines PPDM protocols with applications for banking data mining. Doi: 10.28991/ESJ-2022-06-06-014 Full Text: PDF

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Section: -Results and Discussionmentioning

confidence: 99%