Analysis of Internal Fraud in the Microloan Process with Confirmatory Factor Analysis (CFA) and the Extreme Gradient Boosting (XGBoost) Method

Heri Supriyadi (), Dominicus Savio Priyarsono, Dominicus Savio Priyarsono and Trias Andati
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Heri Supriyadi: IPB University, School of Business
Dominicus Savio Priyarsono: IPB University, Department of Economics
Dominicus Savio Priyarsono: IPB University, School of Business
Trias Andati: IPB University, School of Business

A chapter in Proceedings of the Business Innovation and Engineering Conference (BIEC 2022), 2023, pp 238-255 from Springer

Abstract: Abstract Internal fraud in the microcredit business process has caused significant losses to the banking industry and financial institutions. Internal fraud is one type of operational risk frequently faced by banks/financial institutions focusing on microcredit services. The typical fraud frequently found is Corruption and Misappropriation of Assets (ACFE). For instance, ‘Tempilan’ credit, Mask credit, and Fictitious Credit. Machine learning automatically predicts the possibility of internal fraud in the microloan business process. This research aimed to determine which the most dominant component from the theory of social identity and the fraud triangle that influence someone to commit fraud is. The research used the CFA (Confirmatory Factor Analysis) method and the Extreme Gradient Boosting (XGBoost) method to predict the possibility of fraud. The results revealed that the fraud incident conducted by Relationship Managers (RM) is caused by rationalization factors. The Path diagram of the Second Order CFA fraud risk showed the marketer that the most significant factor loading value was in rationalization, followed by pressure and opportunity. Hence, the results of this research indicated that rationalization had the most significant influence on the fraud risk committed by RM. Therefore It was recommended that the bank or financial institution put its focus more on rationalization matters.

Keywords: CFA; Fraud; Mitigation; Prevention; Prediction; XGBoost (search for similar items in EconPapers)
Date: 2023
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DOI: 10.2991/978-94-6463-144-9_23

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