A financial fraud detection indicator for investors: an IDeA

Bernard, Philippe; Freitas, Najat El Mekkaoui de; Maillet, Bertrand

doi:10.1007/s10479-019-03360-6

Cited by 7 publications

(5 citation statements)

References 49 publications

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“…The ability of Zipf's law as a valuable tool to detect the presence of frauds has also been confirmed by Pietronero et al (2001). Detection systems have been studied by many researchers who have sought innovative solutions based on different data mining techniques such as machine learning, neural networks and clustering analysis (see, e.g., Bernard et al, 2019;Boros et al, 2011;Duan et al, 2009;Jiang et al, 2018;Ngai et al, 2011).…”

Section: Introductionmentioning

confidence: 93%

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

2022

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This paper merges the statistical analysis of data regularities and decision support systems for investors. Specifically, it discusses the Benford’s law as a decision support device for financial investments. In particular, we illustrate the role of such a property of financial data as risk predictor for financial markets. First of all, we show empirical evidence of accordance between data on market index daily returns and Benford’s law. Then, we highlight that on short time period (1 year) the deviations from Benford’s law are related to low risk and positive trend periods; the p value of the $$\chi ^2$$ χ 2 test against the Benford’s distribution displays some predicting power for the market average return and risk level.

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Section: Introductionmentioning

confidence: 93%

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

2022

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“…Machine learning models continuously learn from data, allowing them to adapt to new and evolving fraud tactics, making them valuable tools in the fight against financial fraud. Innovative operations research methodology can also be a useful fraud detection tool (Bernard et al, 2022).…”

Section: Forensic Accounting Techniques and Toolsmentioning

confidence: 99%

“…As Dohrer and Mayes (2020) point out "COVID-19 presents a veritable "perfect storm" for fraud risk". Frauds related to fake medical supplies, counterfeit personal protective equipment (PPE), and fraudulent investment schemes promising pandemic-related profits have surfaced (Andrei, 2021). Criminals have capitalized on the chaos to launch phishing attacks, capitalizing on people's search for information and assistance.…”

Section: Covid-19 and Fraudulent Activitiesmentioning

confidence: 99%

Investigating Financial Frauds in the Modern Landscape: A Forensic Accounting Perspective in the Covid-19 Era

Pavlović,

Đukić,

Gligorić

2023

FU Econ Org

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This paper explores the evolving landscape of forensic accounting, particularly in the context of changing financial frauds and the challenges posed by the COVID-19 pandemic. The progression of financial fraud over time, driven by technological advancements and shifting business environments, highlights the necessity for adaptive and innovative approaches to fraud detection and prevention. Advanced methodologies, including data analytics, digital forensics, and artificial intelligence, have empowered forensic accountants to confront increasingly sophisticated fraud schemes. The examination of frauds arising from the pandemic underscores the resilience of fraudsters in exploiting vulnerabilities during crises. This paper underscores the importance of forensic accounting in upholding financial integrity, ethical standards, and business resilience. It calls for continuous research, innovation, and vigilance in the field of forensic accounting to counter emerging fraud schemes and evolving business landscapes.

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“…In many financial applications it is necessary to filter out unusual observations to ensure the reliability of the system and prevent attempts to maliciously use it. This is particularly useful for detecting financial fraud attempts, as their behaviour patterns differ significantly from normal financial transactions (Bernard et al, 2021). Outlier detection methods are capable of processing all available data in real time to uncover patterns that evade traditional supervised learning methods.…”

Section: Introductionmentioning

confidence: 99%

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

2022

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Mobile payment systems are becoming more popular due to the increase in the number of smartphones, which, in turn, attracts the interest of fraudsters. Extant research has therefore developed various fraud detection methods using supervised machine learning. However, sufficient labeled data are rarely available and their detection performance is negatively affected by the extreme class imbalance in financial fraud data. The purpose of this study is to propose an XGBoost-based fraud detection framework while considering the financial consequences of fraud detection systems. The framework was empirically validated on a large dataset of more than 6 million mobile transactions. To demonstrate the effectiveness of the proposed framework, we conducted a comparative evaluation of existing machine learning methods designed for modeling imbalanced data and outlier detection. The results suggest that in terms of standard classification measures, the proposed semi-supervised ensemble model integrating multiple unsupervised outlier detection algorithms and an XGBoost classifier achieves the best results, while the highest cost savings can be achieved by combining random under-sampling and XGBoost methods. This study has therefore financial implications for organizations to make appropriate decisions regarding the implementation of effective fraud detection systems.

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A financial fraud detection indicator for investors: an IDeA

Cited by 7 publications

References 49 publications

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

Statistical methods for decision support systems in finance: how Benford’s law predicts financial risk

Investigating Financial Frauds in the Modern Landscape: A Forensic Accounting Perspective in the Covid-19 Era

Fraud Detection in Mobile Payment Systems using an XGBoost-based Framework

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