Leveraging Financial Social Media Data for Corporate Fraud Detection

Dong, Wei; Liao, Stephen Shaoyi; Zhang, Zhongju

doi:10.1080/07421222.2018.1451954

Cited by 144 publications

(74 citation statements)

References 49 publications

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“…However, cross-validation and plausibility checks can only reduce the manipulation risk. Similar to financial auditing, a "detection risk" (Dong, Liao, & Zhang, 2018;Hogan & Wilkins, 2008) remains, which depicts the probability that manipulations are not detected. In summary, with the implementation of DP1, it can be certified that data was not manipulated on its way from the source to the sink, so that data producers can be made accountable for the data they provide.…”

Section: Dp2: Sensor Data Is Certified On the Basis Of Cross-validationmentioning

confidence: 99%

"Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data"

Chanson¹,

Bogner²,

Bilgeri³

et al. 2019

JAIS

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A constantly growing pool of smart, connected Internet of Things (IoT) devices poses completely new challenges for business regarding security and privacy. In fact, the widespread adoption of smart products might depend on the ability of organizations to offer systems that ensure adequate sensor data integrity while guaranteeing sufficient user privacy. In light of these challenges, previous research indicates that blockchain technology may be a promising means to mitigate issues of data security arising in the IoT. Building upon the existing body of knowledge, we propose a design theory, including requirements, design principles, and features, for a blockchain-based sensor data protection system (SDPS) that leverages data certification. We then design and develop an instantiation of an SDPS (CertifiCar) in three iterative cycles that prevents the fraudulent manipulation of car mileage data. Furthermore, we provide an ex-post evaluation of our design theory considering CertifiCar and two additional use cases in the realm of pharmaceutical supply chains and energy microgrids. The evaluation results suggest that the proposed design ensures the tamper-resistant gathering, processing, and exchange of IoT sensor data in a privacy-preserving, scalable, and efficient manner.

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Section: Dp2: Sensor Data Is Certified On the Basis Of Cross-validationmentioning

confidence: 99%

"Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data"

Chanson¹,

Bogner²,

Bilgeri³

et al. 2019

JAIS

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show abstract

“…The use of text data in detecting FSF is relatively small at 13% and has been limited to text from the annual reports. In addition, Dong et al (2018) used social media data to detect FSF and found that using this data can aid in FSF detection. Using more of the text in the financial report, including the composition of directors and how these directors and companies link to other companies, could prove to be useful in detecting FSF.…”

Section: Methodsmentioning

confidence: 99%

“…As shown in Table 5, combining text and financial variables in the detection of FSF has only been explored since 2017, with good results (Dong et al, 2018;Yao et al, 2018). This approach is still relatively new and is yet to be considered within the South African context.…”

Section: Methodsmentioning

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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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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“…The study used a classifier approach called the generalized likelihood ratio test (GLRT) and a Bayesian-based application, and they found that the financial numbers, linguistic behavior, and non-verbal cues provide complementary information for fraud detection. Dong et al (2018) used unstructured data (from social media sources Seeking Alpha and Yahoo Finance) in a 64-firm matched set to propose a framework to detect fraud. They found that latent features in social media data are lead indicators of fraud, better than MD&A sections and financial ratios.…”

Section: Articles Published By Journalmentioning

confidence: 99%

“…These articles were also published more recently. Throckmorton et al (2015) was cited ten times and Dong et al (2018) was cited eight times.…”

Section: Articles Published By Journalmentioning

confidence: 99%

An Analysis of Information Systems Literature: Contributions to Fraud Research

Grandstaff

Solsma

2019

AFR

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This study analyzes the knowledge and methods used in information systems (IS) journals in the area of financial statement fraud. The purpose of this analysis is to provide tools and ideas to support interdisciplinary research in accounting and information systems for financial statement fraud topics. The study presents an analysis of five top ranking IS journals (MIS Quarterly, Information Systems Research, Communications of the ACM, Management Science, and Journal of MIS) and five top ranking IS conferences [International Conference on Information Systems (ICIS), Hawaii International Conference on System Sciences (HICSS), International Federation for Information Processing (IFIP), International Conference on Decision Support Systems (DSS), and Decision Sciences Institute National Conference (DSI)]. The literature found from these sources are categorized and presented by year, journal, contribution, type of study, methodology, data set usage, and research design. Although the literature varies, a common thread in many studies is the use of data mining and/or machine learning models to detect fraud.

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Leveraging Financial Social Media Data for Corporate Fraud Detection

Cited by 144 publications

References 49 publications

"Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data"

"Blockchain for the IoT: Privacy-Preserving Protection of Sensor Data"

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

An Analysis of Information Systems Literature: Contributions to Fraud Research

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