Financial misstatement detection: a realistic evaluation

In this work, we examine the evaluation process for the task of detecting financial reports with a high risk of containing a misstatement. This task is often referred to, in the literature, as ``misstatement detection in financial reports''. We provide an extensive review of the related literature. We propose a new, realistic evaluation framework for the task which, unlike a large part of the previous work: (a) focuses on the misstatement class and its rarity, (b) considers the dimension of time when splitting data into training and test and (c) considers the fact that misstatements can take a long time to detect. Most importantly, we show that the evaluation process significantly affects system performance, and we analyze the performance of different models and feature types in the new realistic framework.Comment: 9 pages, ICAIF202

Comparing the performance of oversampling techniques for imbalanced learning in insurance fraud detection

Dissertation presented as the partial requirement for obtaining a Master's degree in Data Science and Advanced AnalyticsAlthough the current trend of data production is focused on generating tons of it every second, there are situations where the target category is represented extremely unequally, giving rise to imbalanced datasets, analyzing them correctly can lead to relevant decisions that produces appropriate business strategies. Fraud modeling is one example of this situation: it is expected less fraudulent transactions than reliable ones, predict them could be crucial for improving decisions and processes in a company. However, class imbalance produces a negative effect on traditional techniques in dealing with this problem, a lot of techniques have been proposed and oversampling is one of them. This work analyses the behavior of different oversampling techniques such as Random oversampling, SOMO and SMOTE, through different classifiers and evaluation metrics. The exercise is done with real data from an insurance company in Colombia predicting fraudulent claims for its compulsory auto product. Conclusions of this research demonstrate the advantages of using oversampling for imbalance circumstances but also the importance of comparing different evaluation metrics and classifiers to obtain accurate appropriate conclusions and comparable results

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1819/thumbnail.jp

Revenue Accrual Quality as an Indicator of Financial Statement Fraud

This study was conducted to address the need for additional financial statement fraud detection techniques. Accruals were chosen as the focus of this study due to the high likelihood of financial statement manipulation using accruals. Using the Dechow/Dichev accrual quality model, this study tested whether or not accrual quality can be used as an indicator of financial statement fraud. The study concluded that the Dechow/Dichev model found non-fraudulent financial statements to have higher quality accruals than fraudulent financial statements. In addition, accrual quality of non-fraudulent financial statements was found to be significantly different from the accrual quality of fraudulent financial statements. Therefore, accrual quality may be considered an indicator of fraudulent financial statement activity

The detection of fraudulent financial statements using textual and financial data

Das Vertrauen in die Korrektheit veröffentlichter Jahresabschlüsse bildet ein Fundament für funktionierende Kapitalmärkte. Prominente Bilanzskandale erschüttern immer wieder das Vertrauen der Marktteilnehmer in die Glaubwürdigkeit der veröffentlichten Informationen und führen dadurch zu einer ineffizienten Ressourcenallokation. Zuverlässige, automatisierte Betrugserkennungssysteme, die auf öffentlich zugänglichen Daten basieren, können dazu beitragen, die Prüfungsressourcen effizienter zuzuweisen und stärken die Resilienz der Kapitalmärkte indem Marktteilnehmer stärker vor Bilanzbetrug geschützt werden. In dieser Studie steht die Entwicklung eines Betrugserkennungsmodells im Vordergrund, welches aus textuelle und numerische Bestandteile von Jahresabschlüssen typische Muster für betrügerische Manipulationen extrahiert und diese in einem umfangreichen Aufdeckungsmodell vereint. Die Untersuchung stützt sich dabei auf einen umfassenden methodischen Ansatz, welcher wichtige Probleme und Fragestellungen im Prozess der Erstellung, Erweiterung und Testung der Modelle aufgreift. Die Analyse der textuellen Bestandteile der Jahresabschlüsse wird dabei auf Basis von Mehrwortphrasen durchgeführt, einschließlich einer umfassenden Sprachstandardisierung, um erzählerische Besonderheiten und Kontext besser verarbeiten zu können. Weiterhin wird die Musterextraktion um erfolgreiche Finanzprädiktoren aus den Rechenwerken wie Bilanz oder Gewinn- und Verlustrechnung angereichert und somit der Jahresabschluss in seiner Breite erfasst und möglichst viele Hinweise identifiziert. Die Ergebnisse deuten auf eine zuverlässige und robuste Erkennungsleistung über einen Zeitraum von 15 Jahren hin. Darüber hinaus implizieren die Ergebnisse, dass textbasierte Prädiktoren den Finanzkennzahlen überlegen sind und eine Kombination aus beiden erforderlich ist, um die bestmöglichen Ergebnisse zu erzielen. Außerdem zeigen textbasierte Prädiktoren im Laufe der Zeit eine starke Variation, was die Wichtigkeit einer regelmäßigen Aktualisierung der Modelle unterstreicht. Die insgesamt erzielte Erkennungsleistung konnte sich im Durchschnitt gegen vergleichbare Ansätze durchsetzen.Fraudulent financial statements inhibit markets allocating resources efficiently and induce considerable economic cost. Therefore, market participants strive to identify fraudulent financial statements. Reliable automated fraud detection systems based on publically available data may help to allocate audit resources more effectively. This study examines how quantitative data (financials) and corporate narratives, both can be used to identify accounting fraud (proxied by SEC’s AAERs). Thereby, the detection models are based upon a sound foundation from fraud theory, highlighting how accounting fraud is carried out and discussing the causes for companies to engage in fraudulent alteration of financial records. The study relies on a comprehensive methodological approach to create the detection model. Therefore, the design process is divided into eight design and three enhancing questions, shedding light onto important issues during model creation, improving and testing. The corporate narratives are analysed using multi-word phrases, including an extensive language standardisation that allows to capture narrative peculiarities more precisely and partly address context. The narrative clues are enriched by successful predictors from company financials found in previous studies. The results indicate a reliable and robust detection performance over a timeframe of 15 years. Furthermore, they suggest that text-based predictors are superior to financial ratios and a combination of both is required to achieve the best results possible. Moreover, it is found that text-based predictors vary considerably over time, which shows the importance of updating fraud detection systems frequently. The achieved detection performance was slightly higher on average than for comparable approaches

CPA\u27s handbook of fraud and commercial crime prevention

https://egrove.olemiss.edu/aicpa_guides/1820/thumbnail.jp

Auditing Symposium XII: Proceedings of the 1994 Deloitte & Touche/University of Kansas Symposium on Auditing Problems

Discussant\u27s response to The Acme Financial Statement Insurance Company Inc: A case study / Dan A. Simunic; Behavioral-economics approach to auditors\u27 risk assessments / William S. Waller; Discussant\u27s response to A Behavioral-economics approach to auditors\u27 risk assessments / Peter R. Gillett; Auditing for fraud: Perception vs reality / Alan J. Winters, John B. Sullivan; What we can learn from Yogi Berra: Discussant\u27s response to Auditing for fraud: Perception vs reality / Karen V. Pincus; What\u27s really wrong with the accounting profession? / A. A. Sommer; Client acceptance and continuation decisions / Stephen Asare, Karl Hackenbrack, W. Robert Knechel; Discussant\u27s response to Accounting and auditing history: Major developments in England and the United States from ancient roots through the mid-twentieth century / G. William Graham ; Exploratory analysis of the determinants of audit engagement resource allocations / Timothy B. Bell, W. Robert Knechel, John J. Willingham; Discussant\u27s response to An Exploratory analysis of the determinants of audit engagement resource allocations / Jane F. Mutchler; Investigation of adaptability in evidential planning / Janice D. DiPietro, Theodore J. Mock, Arnold Wright; Accounting and auditing history: Major developments in England and the United States from ancient roots through the mid-twentieth century / Howard Stettler; Discussant\u27s response to An Investigation of adaptability in evidential planning / Norman R. Walker; Acme Financial Statement Insurance Company Inc: A case study / Stephen J. Aldersleyhttps://egrove.olemiss.edu/dl_proceedings/1011/thumbnail.jp

FRAUD AND BUSINESS CYCLE: EMPIRICAL EVIDENCE FROM FRAUDSTERS AND FRAUD MANAGERS IN NIGERIA

This paper aims at investigating the link between fraud and business cycle in Nigeria using primary data sourced from questionnaires administered on both fraudsters and fraud managers. This is premised on the ground that Nigeria is in recession and has been recently described as fantastically corrupt. Understanding the link between fraud and economic behaviour would give an in depth understanding of fraud levels in the different phases of the Nigerian economy and would help the fraud management system in Nigeria which is believed to have great consequences on the nation's economy. Our result shows that though there is a significant relationship between fraud and business cycle in Nigeria, the level of fraud committed does not solely depend on either expansion or recession exists in the economy, rather, there is an identified range of fraud that might be increased in adverse economy