An Ameliorated hybrid model for Fraud Detection based on Tree based algorithms and Benford's Law

Kurien, Kaithekuzhical Leena; Chikkamannur, Ajeet A.

doi:10.1109/icaecc50550.2020.9339471

Cited by 3 publications

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“…This suggested that concealed manipulation practices in the data remained undetected or the audited organization might be free from manipulation practices. Similar circumstances were recorded by several previous studies, as shown by Kaithekuzhical et al [14], suggesting that the frequency matches might lead to a potential halt in the analysis process (exiting the algorithm). On the other hand, Prasetyo et al [12] argued that a match between Benford Law frequency and the data did not guarantee the absence of manipulation.…”

Section: A the Analytical Results Shows The Possibility Of Experienci...supporting

confidence: 84%

“…In various previous studies, Benford Law served as a tool for detecting fraud, with a prevailing trend in the field of audit to show fraud financial transaction. Benford Law had been used to identify fraud in various contexts, including online marketplace shopping transaction [14], tax payments [12], and financial transaction in the public sector [13]. Setyawan [13] showed that applying Benford Law to detect fraud in financial transaction resulted in a detection probability ranging from 20% to 50%.…”

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

“…This traditional application of Benford Law alone was not entirely effective in fraud detection, implying that transaction data might appear free from manipulation. In this situations, some experts suggested halting the analysis process due to the absence of potential irregularities [14]. However, Prasetyo [12] argued that conformity did not necessarily indicate the absence of data manipulation, rather it encouraged auditors to exercise greater care when handling financial transaction data during audit.…”

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

“…Concerning the evaluation of models used in detection analysis, some studies omitted the assessments and relied solely on analyzing the conformity of Benford Law frequency with data. Meanwhile, the evaluation model of the Hybrid Model Algorithm Framework, as proposed by Kaithekuzhical et al [14], included performance testing of the model based on three machine learning algorithms, namely Random Forest, Logistic Regression, and XG Boost. This model served as an evaluation criterion, comparing the use of standard Benford Law with Multiple Benford Law Model.…”

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

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The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Wiryadinata,

Sugiharto,

Tarno

2023

J. Inf. Syst. Eng. Bus. Intell.

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Background: Fraud in financial transaction is at the root of corruption issues recorded in organization. Detecting fraud practices has become increasingly complex and challenging. As a result, auditors require precise analytical tools for fraud detection. Grouping financial transaction data using K-Means Clustering algorithm can enhance the efficiency of applying Benford Law for optimal fraud detection. Objective: This study aimed to introduce Multiple Benford Law Model for the analysis of data to show potential concealed fraud in the audited organization financial transaction. The data was categorized into low, medium, and high transaction values using K-Means Clustering algorithm. Subsequently, it was reanalyzed through Multiple Benford Law Model in a specialized fraud analysis tool. Methods: In this study, the experimental procedures of Multiple Benford Law Model designed for public sector organizations were applied. The analysis of suspected fraud generated by the toolkit was compared with the actual conditions reported in audit report. The financial transaction dataset was prepared and grouped into three distinct clusters using the Euclidean distance equation. Data in these clusters was analyzed using Benford Law, comparing the frequency of the first digit’s occurrence to the expected frequency based on Benford Law. Significant deviations exceeding ±5% were considered potential areas for further scrutiny in audit. Furthermore, the analysis were validated by cross-referencing the result with the findings presented in the authorized audit organization report. Results: Multiple Benford Law Model developed was incorporated into an audit toolkit to automated calculations based on Benford Law. Furthermore, the datasets were categorized using K-Means Clustering algorithm into three clusters representing low, medium, and high-value transaction data. Results from the application of Benford Law showed a 40.00% potential for fraud detection. However, when using Multiple Benford Law Model and dividing the data into three clusters, fraud detection accuracy increased to 93.33%. The comparative results in audit report indicated a 75.00% consistency with the actual events or facts discovered. Conclusion: The use of Multiple Benford Law Model in audit toolkit substantially improved the accuracy of detecting potential fraud in financial transaction. Validation through audit report showed the conformity between the identified fraud practices and the detected financial transaction. Keywords: Fraud Detection, Benford’s Law, K-Means Clustering, Audit Toolkit, Fraudulent Practices.

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Section: A the Analytical Results Shows The Possibility Of Experienci...supporting

confidence: 84%

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

Section: A the Analytical Results Shows The Possibility Of Experienci...mentioning

confidence: 99%

See 2 more Smart Citations

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Wiryadinata,

Sugiharto,

Tarno

2023

J. Inf. Syst. Eng. Bus. Intell.

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Uncovering Manipulated Files Using Mathematical Natural Laws

Fernandes,

Ó Ciardhuáin,

Antunes

2023

Lecture Notes in Computer Science

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Data Characteristic Stability Based Random Forest Implementation of Credit Card Fraud Detection

Mugundhan

Venkataramanan

2022

2022 5th International Conference on Contemporary Computing and Informatics (IC3I)

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An Ameliorated hybrid model for Fraud Detection based on Tree based algorithms and Benford's Law

Cited by 3 publications

References 0 publications

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

The Use of Machine Learning to Detect Financial Transaction Fraud: Multiple Benford Law Model for Auditors

Uncovering Manipulated Files Using Mathematical Natural Laws

Data Characteristic Stability Based Random Forest Implementation of Credit Card Fraud Detection

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