Detecting fraudulent behavior in banking services: A modified Fraud Pentagon Theory integrating whistleblowing systems and banking fintech security

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Type of the article: Research Article

Abstract
Fraudulent behavior in banking services remains a critical challenge in Indonesia, especially amid rapid digitalization and increasing fintech adoption, while traditional models such as the Fraud Pentagon Theory (FPT) are limited in explaining new fraud drivers related to technological exposure and cultural dynamics. This study aims to detect fraudulent behavior in Indonesian banking services by modifying the FPT through the integration of prestige culture and fintech usage. It employed a survey approach targeting front-office employees of private national banks in Indonesia with at least two years of professional customer service experience. This sample was chosen because such employees are directly exposed to operational pressures, opportunities, and technological systems, making them highly relevant for detecting fraud risk factors. Data were collected using offline and online questionnaires and analyzed with Structural Equation Modeling (SEM) using WarpPLS. The results indicate that the seven factors of pressure (β = 0.916; p < .001), opportunity (β = 0.929; p < .001), rationalization (β = 0.847; p < .001), capability (β = 0.862; p < .001), control (β = 0.907; p < .001), fintech usage (β = 0.712; p < .001), and prestige culture (β = 0.837; p < .001) are significant in determining fraudulent activities, whereas the whistleblowing system (β = –0.312; p = .002) and fintech security usage (β = –0.298; p = .003) are moderators. Based on the findings, the conclusion is that pressure and opportunity are the most potent predictors of fraudulent activity, while whistleblowing and fintech security systems are important mechanisms for preventing such activity.

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    • Figure 1. Research model
    • Figure 2. Second-order model MFPT
    • Figure 3. Model structural SEM
    • Table 1. Demographic characteristics of respondents
    • Table 2. Reliability and validity of constructs
    • Table 3. Path coefficients and significance
    • Table 4. Moderating role of whistleblowing and fintech security
    • Table 5. Reliability and validity test
    • Table 6. Model fit indices summary
    • Table 7. Hypotheses testing results
    • Table B1. X1: Pressure
    • Table B2. X2: Opportunity
    • Table B3. X3: Rationalization
    • Table B4. X4: Capability
    • Table B5. X5: Supervision
    • Table B6. X6: Fintech Usage
    • Table B7. X7: Individual prestige culture
    • Table B8. Z1: Fintech security (Moderator 1)
    • Table B9. Z2: Whistleblowing Systems (Moderator 2)
    • Table B10. Y: Fraud behavior detection (Dependent variable)
    • Conceptualization
      Soni Agus Irwandi, Agus Samekto, Nanang Shonhadji
    • Data curation
      Soni Agus Irwandi, Nanang Shonhadji
    • Formal Analysis
      Soni Agus Irwandi, Agus Samekto, Nanang Shonhadji
    • Funding acquisition
      Soni Agus Irwandi, Agus Samekto
    • Investigation
      Soni Agus Irwandi, Agus Samekto
    • Methodology
      Soni Agus Irwandi
    • Project administration
      Soni Agus Irwandi, Agus Samekto, Nanang Shonhadji
    • Resources
      Soni Agus Irwandi, Agus Samekto, Nanang Shonhadji
    • Software
      Soni Agus Irwandi
    • Supervision
      Soni Agus Irwandi
    • Validation
      Soni Agus Irwandi, Agus Samekto
    • Visualization
      Soni Agus Irwandi, Agus Samekto
    • Writing – original draft
      Soni Agus Irwandi, Nanang Shonhadji
    • Writing – review & editing
      Soni Agus Irwandi, Agus Samekto, Nanang Shonhadji