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Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning.

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Title: Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning.
Authors: Roy, Neha Chhabra1 (AUTHOR) Nehang201112@gmail.com, Prabhakaran, Sreeleakha1 (AUTHOR)
Source: Behaviour & Information Technology. Apr2024, Vol. 43 Issue 5, p876-906. 31p.
Subject Terms: *Banking laws, *Corruption, *Organizational behavior, *Risk assessment, *Data security, *Computers, *Focus groups, *Data security failures, *Interviewing, *Debt, *Questionnaires, *Artificial intelligence, *Identity theft, *Security systems, *Financial stress, *Job stress, *Artificial neural networks, *Algorithms, Fraud prevention, Random forest algorithms, Logistic regression analysis, Research methodology, Conceptual structures, Machine learning
Geographic Terms: India
Abstract: This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]
Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
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  Data: Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning.
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  Data: <searchLink fieldCode="AR" term="%22Roy%2C+Neha+Chhabra%22">Roy, Neha Chhabra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> Nehang201112@gmail.com</i><br /><searchLink fieldCode="AR" term="%22Prabhakaran%2C+Sreeleakha%22">Prabhakaran, Sreeleakha</searchLink><relatesTo>1</relatesTo> (AUTHOR)
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  Data: <searchLink fieldCode="JN" term="%22Behaviour+%26+Information+Technology%22">Behaviour & Information Technology</searchLink>. Apr2024, Vol. 43 Issue 5, p876-906. 31p.
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  Data: <searchLink fieldCode="DE" term="%22India%22">India</searchLink>
– Name: Abstract
  Label: Abstract
  Group: Ab
  Data: This paper explores the different insider employee-led cyber frauds (IECF) based on the recent large-scale fraud events of prominent Indian banking institutions. Examining the different types of fraud and appropriate control measures will protect the banking industry from fraudsters. In this study, we identify and classify Cyber Fraud (CF), map the severity of the fraud on a scale of priority, test the mitigation effectiveness, and propose optimal mitigation measures. The identification and classification of CF losses were based on a literature review and focus group discussions with risk and vigilance officers and cyber cell experts. The CF was analyzed using secondary data. We predicted and prioritized CF based on machine learning-derived Random Forest (RF). An efficient fraud mitigation model was developed based on an offender-victim-centric approach. Mitigation is advised both before and after fraud occurs. Through the findings of this research, banks and fraud investigators can prevent CF by detecting it quickly and controlling it on time. This study proposes a structured, sustainable CF mitigation plan that protects banks, employees, regulators, customers, and the economy, thus saving time, resources, and money. Further, these mitigation measures will improve the reputation of the Indian banking industry and ensure its survival. [ABSTRACT FROM AUTHOR]
– Name: AbstractSuppliedCopyright
  Label:
  Group: Ab
  Data: <i>Copyright of Behaviour & Information Technology is the property of Taylor & Francis Ltd and its content may not be copied or emailed to multiple sites or posted to a listserv without the copyright holder's express written permission. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
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RecordInfo BibRecord:
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      – Type: doi
        Value: 10.1080/0144929X.2023.2191748
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      – Code: eng
        Text: English
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        PageCount: 31
        StartPage: 876
    Subjects:
      – SubjectFull: Banking laws
        Type: general
      – SubjectFull: Corruption
        Type: general
      – SubjectFull: Organizational behavior
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      – SubjectFull: Risk assessment
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      – SubjectFull: Data security
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      – SubjectFull: Computers
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      – SubjectFull: Focus groups
        Type: general
      – SubjectFull: Data security failures
        Type: general
      – SubjectFull: Interviewing
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      – SubjectFull: Debt
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      – SubjectFull: Questionnaires
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      – SubjectFull: Artificial intelligence
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      – SubjectFull: Identity theft
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      – SubjectFull: Security systems
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      – SubjectFull: Financial stress
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      – SubjectFull: Job stress
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      – SubjectFull: Artificial neural networks
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      – SubjectFull: Algorithms
        Type: general
      – SubjectFull: Fraud prevention
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      – SubjectFull: Random forest algorithms
        Type: general
      – SubjectFull: Logistic regression analysis
        Type: general
      – SubjectFull: Research methodology
        Type: general
      – SubjectFull: Conceptual structures
        Type: general
      – SubjectFull: Machine learning
        Type: general
      – SubjectFull: India
        Type: general
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      – TitleFull: Insider employee-led cyber fraud (IECF) in Indian banks: from identification to sustainable mitigation planning.
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            NameFull: Roy, Neha Chhabra
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            NameFull: Prabhakaran, Sreeleakha
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            – D: 01
              M: 04
              Text: Apr2024
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              Y: 2024
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