FRAUD AND ERROR. AUDITORS' RESPONSIBILITY LEVELS

Are auditors responsible for detecting fraud in the companies they inspect? Most ofthe public thinks they are. Auditors often demur. The auditors' duties for the prevention, detectionand reporting of fraud, other illegal acts and errors is one of the most controversial issues inauditing. This paper reports the findings of a survey that explores the financial report users’perceptions on the extent of fraud in Romania and their perceptions of auditors’ responsibilities indetecting fraud and the related audit procedures. This study also finds that there is a widely heldmisperception of the objective of an audit. This is because, among respondents, a much higherexpectation has been placed on the auditors' duties in detecting and reporting fraud than statute oraudit standards require. The results of the study show unquestionably the existence, with respect todetection of fraud, of a gap between the perception of the respondents and the present statutoryrequirements of auditors.fraud; auditors’ responsibilities; audit expectation gap

Firm corruption in the presence of an auditor

This paper develops a framework to explore firm corruption taking account of interaction with an auditor. The basic idea is that an auditor can provide auditing and other (consultancy) services. The extent of the other services depends on firm profitability. Hence auditor profitability can increase with firm corruption that may provide an incentive to collude in corrupt practices. This basic idea is developed using a game theoretic framework. It is shown that a multiplicity of equilibria exist from stable corruption, through auditor controlled corruption, via multiple equilibria to honesty on behalf of both actors. Following the development of the model various policy options are highlighted that show the difficulty of completely removing corrupt practices

Does Benford's law hold in economic research and forecasting?

First and higher order digits in data sets of natural and socio-economic processes often follow a distribution called Benford's law. This phenomenon has been used in many business and scientific applications, especially in fraud detection for financial data. In this paper, we analyse whether Benford's law holds in economic research and forecasting. First, we examine the distribution of leading digits of regression coefficients and standard errors in research papers, published in Empirica and Applied Economics Letters. Second, we analyse forecasts of GDP growth and CPI inflation in Germany, published in Consensus Forecasts. There are two main findings: The relative frequencies of the first and second digits in economic research are broadly consistent with Benford's law. In sharp contrast, the second digits of Consensus Forecasts exhibit a massive excess of zeros and fives, raising doubts on their information content. --Benford's Law,fraud detection,regression coefficients and standard errors,growth and inflation forecasts

Communication of companies with their surroundings - the manipulation of information and information asymmetry

Creative accounting, the bankruptcy of many companies, and ongoing litigations made rapid rebuilding of investor relations imperative. Growing importance of institutional investors, who have high information needs, also impacted this process. Thus, the needs for communication with investors and reducing information asymmetry problems have become key issues in the capital markets. The traditional model of reporting was based largely on information relating to past events (financial accounting). Commonly, there was inadequate consideration of non-financial information impacting the development of goodwill in the future. Some information was published with considerable delay. This facilitated the use of confidential information by those who had previous access to it.information assymetry; investment advisers; credit rating; aggressive accounting; confidential information; international accounting standards; financial crises;

DETECTING EVIDENCE OF NON-COMPLIANCE IN SELF-REPORTED POLLUTION EMISSIONS DATA: AN APPLICATION OF BENFORD'S LAW

The paper introduces Digital Frequency Analysis (DFA) based on Benford's Law as a new technique for detecting non-compliance in self-reported pollution emissions data. Public accounting firms are currently adopting DFA to detect fraud in financial data. We argue that DFA can be employed by environmental regulators to detect fraud in self-reported pollution emissions data. The theory of Benford's Law is reviewed, and statistical justifications for its potentially widespread applicability are presented. Several common DFA tests are described and applied to North Carolina air pollution emissions data in an empirical example.Benford, digital frequency analysis, pollution monitoring, pollution regulation, enforcement, Environmental Economics and Policy, Q25, Q28,

Assessment of irregularities in organic imports from Ukraine to the EU in 2016, notified in OFIS

The underlying study of this report set out to improve the understanding situation concerning residues found in organic food products exported from Ukraine, and to formulate guidelines for identifying and reducing risks for contamination through non-permitted substances based on the results of an in-depth analysis of those residue cases notified in the European Commission’s Organic Farming Information System (OFIS) in 2016. Not surprisingly, the combination of various factors such as (i) the additional sampling required by the new EU import guidelines, (ii) the growing number of exported organic lots from Ukraine, and (iii) the improved analysis technology, led to an increased total number of cases of irregularities notified in OFIS in comparison to previous years. Nevertheless, the number of irregularities in Ukraine in 2016, notified in OFIS, is moderate (affecting estimated < 1% of all exported consignments from Ukraine). Of the lots affected, two thirds were ultimately released as “organic” after additional investigations had been carried out by the respective export CB. Yet, if analysis results of samples taken by the CB’s prior the export, i.e. from crops during the growing season and from lots before they are released for export are included in the risk assessment, Ukraine and its neighbouring countries do need to be considered as relatively high risk countries in terms of contamination and irregularities. It is further interesting to note that the likeliness of residue findings vary a lot among different CBs. The reasons why some CB’s have a high share of residue findings whereas for others proportionally much less residues are found are unclear and should be the subject of further assessments. One assumption is that some CBs took risk-oriented samples whereas others did not. Sampling during the production process (field/leafs and dust) effectively supports organic integrity. Most CB nevertheless focus on residue free final products. The way a CB responds on detected irregularities, i.e. investigates a case and derives “lessons learnt” is very important. A majority of OFIS cases from Ukrainian exports seems to be linked to insufficient management of handling procedure during the storage processes and the transport. However, drift on the field or the intentional use of unauthorised substances are also potential sources of irregularities related to exports from Ukraine. Apart from those cases for which likely root causes have been identified, no clear explanation for discrepancies between lab results between export and import countries could be found for nearly one third of the Ukrainian OFIS cases. Further investigations should be carried out to help identify the reasons for the relatively large differences between the lab results of samples taken from the same trade lots. It is important to better understand these discrepancies in sample measurements because these may lead to significant negative economic impacts for everyone involved in the value chain, even though no rules may have been broken. Another recommendation resulting from this study is to focus more on detecting potential contaminations on the field during the period of crop cultivation. Special attention should be given here to the testing of leaf sample of crops in which contamination has been detected in the past: rapeseeds, sunflower seeds or high quality milling wheat. CB’s should have guidelines on how and when leaf samples should be best taken. Ukrainian organic operators often complain that all Ukrainian operators are put in the same basket and treated as high-risk suppliers. In response to the stricter regulations imposed on them, operators and experts participating in the International Conference “Improving Integrity of Organic Supply Chains” in Odesa 2017 called for an amendment of the inspection policy. Instead of labelling entire countries as high-risk, focus should rather be placed on risky value chains. Supply chains considered high-risk should be relieved from extra measures, once they have demonstrated consistent compliance