The Efficacy of Financial Ratios for Fraud Detection Using Self Organising Maps

Mongwe, Wilson Tsakane; Malan, Katherine M.

doi:10.1109/ssci47803.2020.9308602

Cited by 15 publications

(33 citation statements)

References 21 publications

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Section: Introductionmentioning

confidence: 99%

“…The use of automated techniques for the analysis and detection of financial statement fraud has been on the increase in the past decade [2,[6][7][8][9][10][11][12][13]. Mongwe and Malan [2,6] outline how artificial intelligence and other automated methods can be used to construct decision supporters tools for various stakeholders. For example, auditors may use the decision support tool to flag entities who are at risks of having committed financial statement fraud and reduce the turn around time of audit amongst other benefits [2,6].…”

Section: Introductionmentioning

confidence: 99%

“…Mongwe and Malan [2,6] outline how artificial intelligence and other automated methods can be used to construct decision supporters tools for various stakeholders. For example, auditors may use the decision support tool to flag entities who are at risks of having committed financial statement fraud and reduce the turn around time of audit amongst other benefits [2,6]. A prime example within the South African context is the AG-SA having to audit all local and provincial government entities at the end of each financial year [1,6].…”

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of local government audit outcomes

2021

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The scandals in publicly listed companies have highlighted the large losses that can result from financial statement fraud and weak corporate governance. Machine learning techniques have been applied to automatically detect financial statement fraud with great success. This work presents the first application of a Bayesian inference approach to the problem of predicting the audit outcomes of financial statements of local government entities using financial ratios. Bayesian logistic regression (BLR) with automatic relevance determination (BLR-ARD) is applied to predict audit outcomes. The benefit of using BLR-ARD, instead of BLR without ARD, is that it allows one to automatically determine which input features are the most relevant for the task at hand, which is a critical aspect to consider when designing decision support systems. This work presents the first implementation of BLR-ARD trained with Separable Shadow Hamiltonian Hybrid Monte Carlo, No-U-Turn sampler, Metropolis Adjusted Langevin Algorithm and Metropolis-Hasting algorithms. Unlike the Gibbs sampling procedure that is typically employed in sampling from ARD models, in this work we jointly sample the parameters and the hyperparameters by putting a log normal prior on the hyperparameters. The analysis also shows that the repairs and maintenance as a percentage of total assets ratio, current ratio, debt to total operating revenue, net operating surplus margin and capital cost to total operating expenditure ratio are the important features when predicting local government audit outcomes using financial ratios. These results could be of use for auditors as focusing on these ratios could potentially speed up the detection of fraudulent behaviour in municipal entities, and improve the speed and quality of the overall audit.

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Bayesian inference of local government audit outcomes

2021

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“…MHMC introduces a magnetic field to HMC in addition to the force field already present HMC. This magnetic field results in faster convergence and lower auto-correlations in the generated samples [19,17,20]. When the magnetic component is removed, MHMC has the same performance as HMC.…”

Section: Introductionmentioning

confidence: 99%

Magnetic Hamiltonian Monte Carlo With Partial Momentum Refreshment

2021

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Magnetic Hamiltonian Monte Carlo (MHMC) has been shown to provide more efficient sampling of the target posterior compared to Hamiltonian Monte Carlo (HMC). It achieves this by utilising a user specified magnetic field and the resultant non-canonical Hamiltonian dynamics. This is important for multi-modal distributions which are common in machine learning. In generating each sample in MHMC and HMC, the auxiliary momentum variable is fully regenerated from a Gaussian distribution. Partially updating the momentum has previously been employed in HMC to improve sampling behaviour. Furthermore, it has been used in the context of sampling using integrator dependent shadow Hamiltonian Monte Carlo methods. In this work, we combine the sampling benefits of non-canonical Hamiltonian dynamics offered by MHMC with partial momentum refreshment to create the Magnetic Hamiltonian Monte Carlo with Partial Momentum Refreshment (PMHMC) algorithm. Numerical experiments across various target posterior distributions show that the proposed method outperforms HMC, MHMC and HMC with partial momentum refreshment across all the metrics considered.

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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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The Efficacy of Financial Ratios for Fraud Detection Using Self Organising Maps

Cited by 15 publications

References 21 publications

Bayesian inference of local government audit outcomes

Bayesian inference of local government audit outcomes

Magnetic Hamiltonian Monte Carlo With Partial Momentum Refreshment

Quantum-Inspired Magnetic Hamiltonian Monte Carlo

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