On the Mental State of Consciousness of Wrongdoing

Mistake about or ignorance of the law does not exculpate in criminal law, except in limited circumstances. Doctrine and theory cognate to this principle are, by now, well developed and understood. But might an actor\u27s awareness of the illegality or wrongfulness of her conduct inculpate — that is, constitute a form of mens rea that establishes or aggravates liability? Trends in recent adjudication in white collar crime suggest that the answer is yes. This article, part of a symposium issue on Adjudicating the Guilty Mind, takes the first pass at describing the mental state of “consciousness of wrongdoing,” assessing its fit with the conceptual architecture of substantive criminal law, and uncovering the many challenges of proof and adjudication that this concept poses. Three conclusions broadly emerge from this initial, and somewhat truncated, inquiry: first, inculpating an actor for adverting to the legal or normative significance of her conduct is an attractive means of dealing with difficult line-drawing problems presented by many white collar offenses; second, the method can be justified on both retributive and deterrent grounds; and third, the practice requires much more thought and precision at the operational level, lest problems inherent in the structure of criminal adjudication be exacerbated in cases in which liability depends on the idea that an actor “knew what she was doing was wrong

On the Mental State of Consciousness of Wrongdoing

Mistake about or ignorance of the law does not exculpate in criminal law, except in limited circumstances. Doctrine and theory cognate to this principle are, by now, well developed and understood. But might an actor\u27s awareness of the illegality or wrongfulness of her conduct inculpate — that is, constitute a form of mens rea that establishes or aggravates liability? Trends in recent adjudication in white collar crime suggest that the answer is yes. This article, part of a symposium issue on Adjudicating the Guilty Mind, takes the first pass at describing the mental state of “consciousness of wrongdoing,” assessing its fit with the conceptual architecture of substantive criminal law, and uncovering the many challenges of proof and adjudication that this concept poses. Three conclusions broadly emerge from this initial, and somewhat truncated, inquiry: first, inculpating an actor for adverting to the legal or normative significance of her conduct is an attractive means of dealing with difficult line-drawing problems presented by many white collar offenses; second, the method can be justified on both retributive and deterrent grounds; and third, the practice requires much more thought and precision at the operational level, lest problems inherent in the structure of criminal adjudication be exacerbated in cases in which liability depends on the idea that an actor “knew what she was doing was wrong

Improving Statistical Language Model Performance with Automatically Generated Word Hierarchies

An automatic word classification system has been designed which processes word unigram and bigram frequency statistics extracted from a corpus of natural language utterances. The system implements a binary top-down form of word clustering which employs an average class mutual information metric. Resulting classifications are hierarchical, allowing variable class granularity. Words are represented as structural tags --- unique $n$-bit numbers the most significant bit-patterns of which incorporate class information. Access to a structural tag immediately provides access to all classification levels for the corresponding word. The classification system has successfully revealed some of the structure of English, from the phonemic to the semantic level. The system has been compared --- directly and indirectly --- with other recent word classification systems. Class based interpolated language models have been constructed to exploit the extra information supplied by the classifications and some experiments have shown that the new models improve model performance.Comment: 17 Page Paper. Self-extracting PostScript Fil

Do Fraudulent Companies Employ Different Linguistic Features in Their Annual Reports? An Empirical Study Using Logistic Regression and Random Forest Methodologies

The use of textual analysis to uncover fraudulent actions in 10-K filings is widespread. The previous studies have looked at the Management Disclosure and Analysis (MD&A) section of annual reports to predict illicit behaviour by analysing the tone of executives, with the majority of those studies dating back 10 years or more. The primary goal of this research is to find patterns in linguistic features of entire annual reports of convicted public businesses, which were found using the Corporate Prosecution Registry database, and compare them to non-fraudulent equivalents in the same industry. The algorithms of logistic regression and random forest are implemented to discover important factors and make accurate predictions. The accuracy rate, ROC-AUC value, and 10-fold cross-validation tools are performed to validate the success of each method. The results of the logistic regression revealed that corrupt organisations utilise a more negative, uncertain, and litigious tone. Furthermore, these businesses employ more words with a high lexical diversity and minimal complexity. Based on the Random Forest machine learning technique, the litigious variable is the most important variable in the prediction of untruthful corporations. Moreover, each of the validation methods demonstrates that the Random Forest methodology outperforms logistic regression.nhhma

CPA\u27s handbook of fraud and commercial crime prevention

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

Gotham city. Predicting ‘corrupted’municipalities with machine learning

The economic costs of white-collar crimes, such as corruption, bribery, embezzlement, abuse of authority, and fraud, are substantial. How to eradicate them is a mounting task in many countries. Using police archives, we apply machine learning algorithms to predict corruption crimes in Italian municipalities. Drawing on input data from 2011, our classification trees correctly forecast over 70 % (about 80 %) of the municipalities that will experience corruption episodes (an increase in corruption crimes) over the period 2012–2014. We show that algorithmic predictions could strengthen the ability of the 2012 Italy's anti-corruption law to fight white-collar delinquencies and prevent the occurrence of such crimes while preserving transparency and accountability of the policymaker

Corporate Ethical Training: An Answer to White-Collar Crimes

The modern business corporation is a culturally significant component of American Society. It is facing a cultural invasion of the highest order. The categorical imperative, an unconditional principle that rational individuals must follow despite natural desires or inclinations to do otherwise, is today being called into question. This is most likely the result of grounding moral values upon information that is transient and unstable rather than upon established data. The social contract, which governs the formation and maintenance of individual morals, is a requirement in organizations that demands collective agency – employees acting together to set forth moral rules of behavior and eschew pernicious leanings and tendencies. From that perspective, ethical training becomes a key leveraging point in the disconnect between cultural expectations and individual behaviors in corporate America

FINANCIAL FRAUD, ECONOMIC OFFENCE IN INDIA: CRIME PREVENTION THROUGH HEURISTIC METHOD

Financial inclusion is the decade’s big achievement of the Government of India through the opening zero balance saving accounts mass level in nationalized scheduled banks. In recent past, India is facing big challenges to tackle the white collar and economic crime problems responsible for ruining the entire economic management and system of public policy allocations. The present paper is analyzing the various publicly concerned financial fraud and multiple economic offenses which are directly or indirectly affecting the country’s economy and responsible for sea-merging the financial condition of the nation. Even, there are multiple preventive provisions implemented to prevent such offences by the government and various initiatives taken parallel to combat the economic crisis. In this paper, a heuristic method of crime prevention has been suggested to tackle similar offences and reduce the occurrence of the frauds

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