A Case Study and Fraud Rate Prediction in e-Banking Systems Using Machine Learning and Data Mining-Reference-Cited by-同舟云学术

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A Case Study and Fraud Rate Prediction in e-Banking Systems Using Machine Learning and Data Mining

Published:2020-11-28 Issue: Volume: Page:71-83
ISSN:2194-5357
Container-title:Advances in Intelligent Systems and Computing
language:
Short-container-title:

Author:

Nuha Musfika,Mahmud Sakib,Sattar Abdus

Publisher

Springer Singapore

Link

http://link.springer.com/content/pdf/10.1007/978-981-15-7394-1_6

Reference20 articles.

1. Mobile financial services data. Bb.org.bd, 2019 [Online]. Available https://www.bb.org.bd/fnansys/paymentsys/mfsdata.php. Accessed: 12 Mar 2019

2. Internet Subscribers in Bangladesh January, 2019. BTRC. Btrc.gov.bd, 2019 [Online]. Available http://www.btrc.gov.bd/content/internet-subscribers-bangladesh-January-2019. Accessed 12 Mar 2019

3. Number of internet users worldwide 2005–2018 Statista, Statista, 2019 [Online]. Available https://www.statista.com/statistics/273018/number-of-internet-users-worldwide/. Accessed 12 Mar 2019

4. Banking services vulnerable to fraud, The Daily Star, 2017 [Online]. Available https://www.thedailystar.net/editorial/banking-services-vulnerable-fraud-1448149. Accessed 12 Mar 2019

5. Jeeva, S., Rajsingh, E.: Intelligent phishing url detection using association rule mining. Human-Centr. Comput. Inf. Sci. 6(1), 9 (2016)

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