Exploring Fraudulent Financial Reporting with GHSOM

Tsaih, Rua‐Huan; Lin, Wan‐Ying; Huang, Shin‐Ying

doi:10.1007/978-3-642-01393-5_5

Cited by 12 publications

(9 citation statements)

References 11 publications

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“…To our knowledge, unsupervised learning approaches have only been explored in Y.-J. Chen (2015), Deng and Mei (2009), Huang, Tsaih, and Lin (2014), Huang, Tsaih, and Yu (2014), Tsaih et al (2009). A thorough comparison of unsupervised learning methods has not been conducted in the FSF literature.…”

Section: Methods Usedmentioning

confidence: 99%

“…Thus, an unsupervised learning approach, which does not require a labelled data set, would more appropriate in the South African context. The unsupervised approach has been used on companies listed on the Taiwan and the Chinese stock exchanges and provided promising results (Deng & Mei, 2009;Huang, Tsaih, & Lin, 2014;Tsaih et al, 2009). As these are emerging markets, using an unsupervised approach could potentially provide good results when applied to a South African data set as South Africa is also an emerging market.…”

Section: Definitionmentioning

confidence: 99%

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A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Mongwe

Malan

2020

SACJ

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Financial statement fraud has been on the increase in the past two decades and includes prominent scandals such as Enron, WorldCom and more recently in South Africa, Steinhoff. These scandals have led to billions of dollars being lost in the form of market capitalisation from different stock exchanges across the world. During this time, there has been an increase in the literature on applying automated methods to detecting financial statement fraud using publicly available data. This paper provides a survey of the literature on automated financial statement fraud detection and identifies current gaps in the literature. The paper highlights a number of important considerations in the implementation of financial statement fraud detection decision support systems, including 1) the definition of fraud, 2) features used for detecting fraud, 3) region of the case study, dataset size and imbalance, 4) algorithms used for detection, 5) approach to feature selection / feature engineering, 6) treatment of missing data, and 7) performance measure used. The current study discusses how these and other implementation factors could be approached within the South African context.

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Section: Methods Usedmentioning

confidence: 99%

Section: Definitionmentioning

confidence: 99%

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Mongwe

Malan

2020

SACJ

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“…To address the weaknesses of SOMs, including the predefined and fixed topology size and the inability to identify hierarchical relations among samples, Dittenbach, Merkl, and Rauber [12] developed the concept of a GHSOM, which addresses the issue of the fixed network architecture of an SOM through a multilayer hierarchical network structure. The flexible and hierarchical features of a GHSOM generate more delicate clustering results than an SOM and make a GHSOM a versatile analysis tool for tasks regarding data mining, image recognition, Web mining, and text mining problems [12,13,38,41,43,46,50]. Tsaih et al [46] used GHSOM to cluster preliminarily non-fraud and fraud financial statements into subgroups with hierarchical relationships.…”

Section: Studymentioning

confidence: 99%

“…The flexible and hierarchical features of a GHSOM generate more delicate clustering results than an SOM and make a GHSOM a versatile analysis tool for tasks regarding data mining, image recognition, Web mining, and text mining problems [12,13,38,41,43,46,50]. Tsaih et al [46] used GHSOM to cluster preliminarily non-fraud and fraud financial statements into subgroups with hierarchical relationships. Their results showed that the GHSOM can be used to classify the samples into high fraud risk groups, mixed groups, and healthy groups.…”

Section: Studymentioning

confidence: 99%

Feature Extraction of Fraudulent Financial Reporting Through Unsupervised Neural Networks

Huang¹,

Tsaih²,

Lin³

2014

NNW

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This tutorial is based on modification of the professor nomination lecture presented two years ago in front of the Scientific Council of the Czech Technical University in Prague [16].It is devoted to the techniques for the models developing suitable for processes forecasting in complex systems. Because of the high sensitivity of the processes to the initial conditions and, consequently, due to our limited possibilities to forecast the processes for the long-term horizon, the attention is focused on the techniques leading to practical applications of the short term prediction models. The aim of this tutorial paper is to bring attention to possible difficulties which designers of the predicting models and their users meet and which have to be solved during the prediction model developing, validation, testing, and applications. The presented overview is not complete, it only reflects the author's experience with developing of the prediction models for practical tasks solving in banking, meteorology, air pollution and energy sector. The paper is completed by an example of the global solar radiation prediction which forms an important input for the electrical energy production forecast from renewable sources. The global solar radiation forecasting is based on numerical weather prediction models. The time-lagged ensemble technique for uncertainty quantification is demonstrated on a simple example.

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“…The inference of complex probabilistic models using Markov Chain Monte Carlo (MCMC) algorithms has become very common [1][2][3][4][5][6][7]. MCMC methods have been successfully applied in a variety of fields including health, finance and cosmology [1,4,5,[8][9][10][11][12][13][14][15]. The goal of MCMC methods is to construct a Markov chain that leaves the target posterior distribution invariant [3,16].…”

Section: Introductionmentioning

confidence: 99%

Quantum-Inspired Magnetic Hamiltonian Monte Carlo

2021

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Hamiltonian Monte Carlo (HMC) is a Markov Chain Monte Carlo algorithm that is able to generate distant proposals via the use of Hamiltonian dynamics, which are able to incorporate first-order gradient information about the target posterior. This has driven its rise in popularity in the machine learning community in recent times. It has been shown that making use of the energy-time uncertainty relation from quantum mechanics, one can devise an extension to HMC by allowing the mass matrix to be random with a probability distribution instead of a fixed mass. Furthermore, Magnetic Hamiltonian Monte Carlo (MHMC) has been recently proposed as an extension to HMC and adds a magnetic field to HMC which results in non-canonical dynamics associated with the movement of a particle under a magnetic field. In this work, we utilise the non-canonical dynamics of MHMC while allowing the mass matrix to be random to create the Quantum-Inspired Magnetic Hamiltonian Monte Carlo (QIMHMC) algorithm, which is shown to converge to the correct steady state distribution. Empirical results on a broad class of target posterior distributions show that the proposed method produces better sampling performance than HMC, MHMC and HMC with a random mass matrix.

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Exploring Fraudulent Financial Reporting with GHSOM

Cited by 12 publications

References 11 publications

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

A Survey of Automated Financial Statement Fraud Detection with Relevance to the South African Context

Feature Extraction of Fraudulent Financial Reporting Through Unsupervised Neural Networks

Quantum-Inspired Magnetic Hamiltonian Monte Carlo

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