Fighting Accounting Fraud through Forensic Analytics

Accounting Fraud is one of the most harmful financial crimes as it often results in massive corporate collapses, commonly silenced by powerful high-status executives and managers. Accounting fraud represents a significant threat to the financial system stability due to the resulting diminishing of the market confidence and trust of regulatory authorities. Its catastrophic consequences expose how vulnerable and unprotected the community is in regards to this matter, since most damage is inflicted to investors, employees, customers and government. Accounting fraud is defined as the calculated misrepresentation of the financial statement information disclosed by a company in order to mislead stakeholders regarding the firm’s true financial position. Different fraudulent tricks can be used to commit accounting fraud, either direct manipulation of financial items or creative methods of accounting, hence the need for non-static regulatory interventions that take into account different fraudulent patterns. Accordingly, this study aims to identify signs of accounting fraud occurrence to be used to, first, identify companies that are more likely to be manipulating financial statement reports, and second, assist the task of examination within the riskier firms by evaluating relevant financial red-flags, as to efficiently recognise irregular accounting malpractices. To achieve this, a thorough forensic data analytic approach is proposed that includes all pertinent steps of a data-driven methodology. First, data collection and preparation is required to present pertinent information related to fraud offences and financial statements. The compiled sample of known fraudulent companies is identified considering all Accounting Series Releases and Accounting and Auditing Enforcement Releases issued by the U.S. Securities and Exchange Commission between 1990 and 2012, procedure that resulted in 1,594 fraud-year observations. Then, an in-depth financial ratio analysis is performed in order to evaluate publicly available financial statement data and to preserve only meaningful predictors of accounting fraud. In particular, two commonly used statistical approaches, including non-parametric hypothesis testing and correlation analysis, are proposed to assess significant differences between corrupted and genuine reports as well as to identify associations between the considered ratios. The selection of a smaller subset of explanatory variables is later reinforced by the implementation of a complete subset logistic regression methodology. Finally, statistical modelling of fraudulent and non-fraudulent instances is performed by implementing several machine learning methods. Classical classifiers are considered first as benchmark frameworks, including logistic regression and discriminant analysis. More complex techniques are implemented next based on decision trees bagging and boosting, including bagged trees, AdaBoost and random forests. In general, it can be said that a clear enhancement in the understanding of the fraud phenomenon is achieved by the implementation of financial ratio analysis, mainly due to the interesting exposure of distinctive characteristics of falsified reporting and the selection of meaningful ratios as predictors of accounting fraud, later validated using a combination of logistic regression models. Interestingly, using only significant explanatory variables leads to similar results obtained when no selection is performed. Furthermore, better performance is accomplished in some cases, which strongly evidences the convenience of employing less but significant information when detecting accounting fraud offences. Moreover, out-of-sample results suggest there is a great potential in detecting falsified accounting records through statistical modelling and analysis of publicly available accounting information. It has been shown good performance of classic models used as benchmark and better performance of more advanced methods, which supports the usefulness of machine learning models as they appropriately meet the criteria of accuracy, interpretability and cost-efficiency required for a successful detection methodology. This study contributes in the improvement of accounting fraud detection in several ways, including the collection of a comprehensive sample of fraud and non-fraud firms concerning all financial industries, an extensive analysis of financial information and significant differences between genuine and fraudulent reporting, selection of relevant predictors of accounting fraud, contingent analytical modelling for better differentiate between non-fraud and fraud cases, and identification of industry-specific indicators of falsified records. The proposed methodology can be easily used by public auditors and regulatory agencies in order to assess the likelihood of accounting fraud and to be adopted in combination with the experience and instinct of experts to lead to better examination of accounting reports. In addition, the proposed methodological framework could be of assistance to many other interested parties, such as investors, creditors, financial and economic analysts, the stock exchange, law firms and to the banking system, amongst others

Driving continuous improvement

The quality of improvement depends on the quality of leading and lagging performance indicators. For this reason, several tools, such as process mapping, cause and effect analysis and FMEA, need to be used in an integrated way with performance measurement models, such as balanced scorecard, integrated performance measurement system, performance prism and so on. However, in our experience, this alone is not quite enough due to the amount of effort required to monitor performance indicators at operational levels. The authors find that IT support is key to the successful implementation of performance measurement-driven continuous improvement schemes

Damage estimation of subterranean building constructions due to groundwater inundation – the GIS-based model approach GRUWAD

The analysis and management of flood risk commonly focuses on surface water floods, because these types are often associated with high economic losses due to damage to buildings and settlements. The rising groundwater as a secondary effect of these floods induces additional damage, particularly in the basements of buildings. Mostly, these losses remain underestimated, because they are difficult to assess, especially for the entire building stock of flood-prone urban areas. For this purpose an appropriate methodology has been developed and lead to a groundwater damage simulation model named GRUWAD. The overall methodology combines various engineering and geoinformatic methods to calculate major damage processes by high groundwater levels. It considers a classification of buildings by building types, synthetic depth-damage functions for groundwater inundation as well as the results of a groundwater-flow model. The modular structure of this procedure can be adapted in the level of detail. Hence, the model allows damage calculations from the local to the regional scale. Among others it can be used to prepare risk maps, for ex-ante analysis of future risks, and to simulate the effects of mitigation measures. Therefore, the model is a multifarious tool for determining urban resilience with respect to high groundwater levels

Operational Research: Methods and Applications

Throughout its history, Operational Research has evolved to include a variety of methods, models and algorithms that have been applied to a diverse and wide range of contexts. This encyclopedic article consists of two main sections: methods and applications. The first aims to summarise the up-to-date knowledge and provide an overview of the state-of-the-art methods and key developments in the various subdomains of the field. The second offers a wide-ranging list of areas where Operational Research has been applied. The article is meant to be read in a nonlinear fashion. It should be used as a point of reference or first-port-of-call for a diverse pool of readers: academics, researchers, students, and practitioners. The entries within the methods and applications sections are presented in alphabetical order. The authors dedicate this paper to the 2023 Turkey/Syria earthquake victims. We sincerely hope that advances in OR will play a role towards minimising the pain and suffering caused by this and future catastrophes

Continuous maintenance and the future – Foundations and technological challenges

High value and long life products require continuous maintenance throughout their life cycle to achieve required performance with optimum through-life cost. This paper presents foundations and technologies required to offer the maintenance service. Component and system level degradation science, assessment and modelling along with life cycle ‘big data’ analytics are the two most important knowledge and skill base required for the continuous maintenance. Advanced computing and visualisation technologies will improve efficiency of the maintenance and reduce through-life cost of the product. Future of continuous maintenance within the Industry 4.0 context also identifies the role of IoT, standards and cyber security