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Food safety risk brought by environmental pollution seriously threatens human health and affects national economic and social development. In particular, heavy metal pollution and nutrient deficiency have caused regional diseases. Thus, the purpose of this paper is to present a risk early warning method of food safety considering environmental and nutritional factors.

A novel risk early warning modelling method based on the long short-term memory (LSTM) neural network integrating sum product based analytic hierarchy process (AHP-SP) is proposed. The data fuzzification method is adopted to overcome the uncertainty of food safety detection data and the processed data are viewed as the input of the LSTM. The AHP-SP method is used to fuse the risk of detection data and the obtained risk values are viewed as the expected output of the LSTM. Finally, the proposed method is applied on one group of sterilized milk data from a food detection agency in China.

The experimental results show that compared with the back propagation and the radial basis function neural networks, the proposed method has higher accuracy in predicting the development trend of food safety risk. Moreover, the causal factors of the risk can be figured out through the predicted results.

The proposed modelling method can achieve accurate prediction and early warning of food safety risk, and provide decision-making basis for the relevant departments to formulate targeted risk prevention and control measures, thereby avoiding food safety incidents caused by environmental pollution or nutritional deficiency.

The number of construction dispute cases has maintained a high growth trend in recent years. The effective exploration and management of construction contract risk can directly promote the overall performance of the project life cycle. The miss of clauses may result in a failure to match with standard contracts. If the contract, modified by the owner, omits key clauses, potential disputes may lead to contractors paying substantial compensation. Therefore, the identification of construction project contract missing clauses has heavily relied on the manual review technique, which is inefficient and highly restricted by personnel experience. The existing intelligent means only work for the contract query and storage. It is urgent to raise the level of intelligence for contract clause management. Therefore, this paper aims to propose an intelligent method to detect construction project contract missing clauses based on Natural Language Processing (NLP) and deep learning technology.

A complete classification scheme of contract clauses is designed based on NLP. First, construction contract texts are pre-processed and converted from unstructured natural language into structured digital vector form. Following the initial categorization, a multi-label classification of long text construction contract clauses is designed to preliminary identify whether the clause labels are missing. After the multi-label clause missing detection, the authors implement a clause similarity algorithm by creatively integrating the image detection thought, MatchPyramid model, with BERT to identify missing substantial content in the contract clauses.

1,322 construction project contracts were tested. Results showed that the accuracy of multi-label classification could reach 93%, the accuracy of similarity matching can reach 83%, and the recall rate and F1 mean of both can reach more than 0.7. The experimental results verify the feasibility of intelligently detecting contract risk through the NLP-based method to some extent.

NLP is adept at recognizing textual content and has shown promising results in some contract processing applications. However, the mostly used approaches of its utilization for risk detection in construction contract clauses predominantly are rule-based, which encounter challenges when handling intricate and lengthy engineering contracts. This paper introduces an NLP technique based on deep learning which reduces manual intervention and can autonomously identify and tag types of contractual deficiencies, aligning with the evolving complexities anticipated in future construction contracts. Moreover, this method achieves the recognition of extended contract clause texts. Ultimately, this approach boasts versatility; users simply need to adjust parameters such as segmentation based on language categories to detect omissions in contract clauses of diverse languages.

The purpose of this study is to investigate the effectiveness of fraud detection instruments in not-for-profit (NFP) organizations. Not-for-profit organizations rely on trust and volunteer support. They are often small in size and do not have relevant expertise to prevent fraud. Such organizations are more vulnerable to fraud and, consequently, require effective fraud detection instruments. The existing literature on fraud detection is primarily descriptive and does not measure instrument performance. The authors address this research gap and provide a detailed overview of the impact of nine common fraud detection instruments.

Data were obtained from an NFP fraud survey conducted in Australia and New Zealand. A set of contingency tables is produced to explore the relationship between the existence of a specific fraud detection instrument and actual detection of fraud. We also investigate the relationship between organization size and fraud detection strategy.

The findings provide valuable insights into understanding fraud detection mechanisms. Although most fraud detection measures may not lead to more fraud detection, three highly effective instruments emerge, namely, fraud control policies, whistle-blower policies and fraud risk registers. The results also reveal that commonly used fraud detection instruments are not necessarily the most effective. This is true in a significant number of small organizations that appear to be focusing on ineffective fraud detection instruments.

Implementation of more effective fraud detection measures will reduce the damage caused to an organization and is highly relevant for practitioners.

The results show that differences in the effectiveness of fraud detection instruments in the NFP sector exist. This knowledge is directly applicable by related organizations to reduce fraud damage.

Conventionally, participation in the informal economy has been explained by viewing citizens as rational economic actors participating when the pay-off is greater than the expected cost of being caught and punished, and thus tackled by raising the sanctions and risks of detection. Given that many citizens do not engage even when the benefits outweigh the costs, a new social actor approach has begun to emerge which explains the informal economy as arising when tax morality is low and seeks to foster commitment to compliance. The purpose of this paper is to provide an evidence-based evaluation of these competing policy approaches.

To do so, the results are reported of 1,306 face-to-face interviews undertaken during 2013 in the UK.

The finding is that raising the sanctions and risks of detection has no significant impact on the likelihood of participation in the informal sector. However, participation in the informal economy is significantly associated with tax morality. Indeed, the only time that increasing the sanctions and risks of detection reduces the level of participation in the informal economy is amongst citizens with very low tax morality.

Rather than continue with the current rational economic actor approach of increasing the penalties and risks of detection, this case study of the UK reveals that a new policy approach is required that seeks to improve tax morality by introducing measures to reduce the acceptability of participating in the informal economy. Whether this is more widely applicable now needs to be tested, given the dominance throughout the world of this punitive rational economic actor approach.

This paper provides evidence supporting a new social actor approach towards explaining and tackling participation in the informal economy.

France is a model of best practice in the European Union as regards policy to combat undeclared work. The purpose of this paper is to take the country as a case study to evaluate the competing explanations of why people engage in undeclared work which underpin such policy, namely, the dominant rational-economic-actor approach and the more recent social-actor approach.

To evaluate these approaches, the results of 1,027 interviews undertaken in 2013 with a representative sample of the French population are analysed.

The finding is that higher perceived penalties and risks of detection have no significant impact on the likelihood of conducting undeclared work in France. In contrast, the level of tax morale has a significant impact on engagement in the activity: the higher the tax morale, the lower is the likelihood of participation in the undeclared economy. Higher penalties and risks of detection only decrease the likelihood of participation in undeclared work amongst the small minority of the French population with very low tax morale.

Current policy in France to counter undeclared work is informed principally by the rational-economic-actor approach based on a highly developed infrastructure for detection and significant penalties alongside incentives to declare small-scale own-account work. The present analysis suggests that this approach needs to be supplemented with measures to improve citizens’ commitment to compliance by enhancing tax morale.

This case study of a country with a well-developed policy framework to combat undeclared work provides evidence to support the social-actor approach for informing policy change.

This paper addresses a fundamental issue in financial regulation ‐ that of the auditor’s ability to detect material irregularities. If an auditor is to detect irregularities he/she must also be cognisant of fraud aetiology by drawing on such other disciplines as psychology, criminology and sociology. The paper first provides a critique of existing fraud aetiology models and then describes the ROP Fraud Risk‐Assessment Model constructed by the author in a study of convicted serious fraud offenders in Australia. The main concern of the paper is with the eclectic fraud detection model (EFD), of which the ROP model is a component. The EFD model is aimed at enhancing the auditor’s fraud detection ability, it has been constructed by the author and its utility successfully tested in Australia in a survey of auditors. Finally, the policy implications for auditors of the findings obtained are also considered.

In this study, we examine the role of temporal framing in the context of tax audit risk. Using construal-level theory, we propose that compared with an every-year frame (e.g., 1.5 million returns are audited every year), framing audit risk in an everyday frame (e.g., 4,000 returns are audited every day) will make audit risk seem more likely and thus increase taxpayer compliance. We test whether perceived fairness of the tax system, an individual difference variable related to tax compliance, moderates the effect of temporal framing on behavioral intentions. The results show that communicating risk in a day frame rather than a year frame increases compliance for taxpayers who perceive the tax system as unfair but not for taxpayers who perceive the tax system as fair. Increasing compliance among taxpayers who perceive the tax system as unfair is crucial, as they are less likely to be compliant. Thus, framing audit risk can assist in increasing taxpayer compliance.

This study investigates whether consideration of future consequences (CFC), Machiavellianism (MACH) and the perceived role of ethics and social responsibility (PRESOR) enhance understanding of the impact of tax audit risk on compliance.

A between-subjects experiment is conducted to test the hypotheses. A hypothetical tax audit case (or lack thereof) is used to create a high (low) perceived tax audit risk. The usable responses of 144 participants representing the general taxpayer population are analyzed.

The results suggest that taxpayers with lower CFC, MACH or PRESOR scores are more compliant when tax audit risk is high than low. In contrast, taxpayers with higher CFC, MACH or PRESOR scores are indifferent toward high or low tax audit risk.

Research can elicit consideration of future consequences of being detected for taxpayers with lower CFC scores to increase compliance. Additionally, increased saliency of tax audit risk and detection of noncompliance in a tax audit can enhance the compliance of taxpayers with lower MACH scores. Dissemination of information via social media on the value of ethical and social responsibility of compliance can also increase the compliance of taxpayers with higher PRESOR scores.

This study helps researchers and the tax authority better understand the complexities of compliance and the ethical dilemmas that taxpayers face, especially when a considerable amount of cash income is involved. To deter underreporting of cash income, the tax authority can use social media to explain how data analytics tools can facilitate the analysis and integration of multiple sources of a taxpayer’s income and expenses.

Prior studies present participants with objective tax audit rates, such as 5, 25 and 30 (Cullis et al., 2006; Maciejovsky et al., 2007; Trivedi et al., 2003) or 50% (Maciejovsky et al., 2012) to investigate tax compliance. However, the actual tax audit rate is very low (about 1%) due to the limited resources of the tax authority (Alm and Torgler, 2011). To attenuate perceptions of unrealistic tax audit rates, this study operationalizes high (low) tax audit risk via a hypothetical tax audit case (or lack thereof) to examine the impact of tax audit risk on compliance.

Internal auditors should consider the risks pertinent to an auditee when planning the work. Internal auditors may select a procedures‐driven approach or a risk‐driven approach. In a procedures‐driven approach, the audit procedures are chosen without full consideration of the risks present. Rather, the internal auditor may use procedures because they are commonly employed or because they were used on the last examination of the auditee. In a risk‐driven approach, specific procedures are planned only after consideration of the risks. A risk‐driven approach is generally more effective and efficient than a procedures‐driven approach because the internal auditor′s efforts are focused on areas with relatively more risk.

Business risk and inherent risk both bear on the audit; the audit risk model; and the nature, timing, and extent of work performed. Inherent risk and business risk bear an inverse relationship to detec‐tion risk and have a direct effect on the level of work performed. Neither risk can be eliminated totally and neither is controllable by the auditor. Business risk relates to the financial statements and affects overall audit risk; inherent risk applies to an individual audit area. Inherent risk is explicitly included in the professional standards and the audit‐risk model while business risk is not and has only an indirect bearing on the model. Management can take steps to affect the level of inherent risk, but the perceptions of users of the financial statements bear on business risk.