Autoencoder Neural Networks versus External Auditors: Detecting Unusual Journal Entries in Financial Statement Audits

With the increasing complexity of business processes in today\u27s organizations and the ever-growing amount of structured accounting data, identifying erroneous or fraudulent business transactions and corresponding journal entries poses a major challenge for public accountants at annual audits. In current audit practice, mainly static rules are applied which check only a few attributes of a journal entry for suspicious values. Encouraged by numerous successful adoptions of deep learning in various domains we suggest an approach for applying autoencoder neural networks to detect unusual journal entries within individual financial accounts. The identified journal entries are compared to a list of entries that were manually tagged by two experienced auditors. The comparison shows high f-scores and high recall for all analyzed financial accounts. Additionally, the autoencoder identifies anomalous journal entries that have been overlooked by the auditors. The results underpin the applicability and usefulness of deep learning techniques in financial statement audits

XAI in the Audit Domain - Explaining an Autoencoder Model for Anomaly Detection

Detecting erroneous or fraudulent business transactions andcorresponding journal entries imposes a significant challenge for auditors duringannual audits. One possible solution to cope with these problems is the use ofmachine learning methods, such as an autoencoder, to identify unusual journalentries within individual financial accounts. There are several methods for theinterpretation of such black-box models, summarized under the term eXplainableArtificial Intelligence (XAI), but these are not suitable for autoencoders. This paperproposes an approach for interpreting autoencoders, which consists of labelingthe journal entries first using the autoencoder and then training models suitablefor the application of XAI methods using these labels. The results obtained areevaluated with the help of human auditors, showing that an autoencoder model is not onlyable to capture relevant features of the domain but also provides additionalvaluable insights for identifying anomalous journal entries

A Step Forward to Unravel Open Histamine H2 Receptor Questions: Synthesis and Biological Evaluation of Novel H2R Ligands Including Radio- and Fluorescence-Labeled Pharmacological Tools

In summary, in this work new, labeled (radioactive or fluorescent) molecular tools for the H2R with improved kinetic properties were discovered. In addition, an exchange of thiazole with thiadiazole in ligands of the carbamoylguanidine class was found to lead to high-affinity, subtype-, and D2-like receptor-selective H2R agonists. The results of this work represent a very important step for future studies of H2R agonists of the carbamoylguanidine type in order to get a better understanding the cellular mechanisms of the H2R in general and specifically its function in the CNS (in vivo)

Histamine H2 receptor radioligands: triumphs and challenges

Since the discovery of the histamine H2 receptor (H2R), radioligands were among the most powerful tools to investigate its role and function. Initially, radiolabeling was used to investigate human and rodent tissues regarding their receptor expression. Later, radioligands gained increasing significance as pharmacological tools in in vitro assays. Although tritium-labeling was mainly used for this purpose, labeling with carbon-14 is preferred for metabolic studies of drug candidates. After the more-or-less successful application of numerous labeled H2R antagonists, the recent development of the G protein-biased radioligand [3H]UR-KAT479 represents another step forward to elucidate the widely unknown role of the H2R in the central nervous system through future studies

Tunable contact angle hysteresis for component placement on stretchable superhydrophobic surfaces

One of the promising strategies to achieve high performance flexible electronics is to integrate high performance components (micro‐electro‐mechanical systems, integrated circuit, etc.) on a flexible substrate. The heterointegration of fragile high performance components, for example, thinned down 100 GHz silicon technology, necessitate however methodologies to place these components on the substrate while exerting as little force as possible to prevent any damage from occurring. In this work, a novel approach is presented for component positioning by capillary assembly on a smart flexible substrate composed of two layers of polymers. It is shown how the wettability of the surface can be engineered by combining stretching induced deformation of the top layer with plasma treatment. Using magnetically actuated ferrofluid droplets which carry the silicon chip shows how it can be aligned and deposited at predetermined sites on these substrates. It is demonstrated that unlike standard capillary alignment which relies on a hydrophobic/hydrophilic contrast, in this case deposition is controlled by surface adhesion contrast between the site and the rest of the substrate. Furthermore, it is explained how deposition sites can be selectively activated through localized stretching thus producing generic smart substrates on which precise depositions sites can be activated according to the needs of the end user

Front-End development of the graphical user interface for NTuple Wizard

The aim of this report is to cover the work done during my placement at CERN as a Summer Student. It is related to the frontend development of NTuple Wizard — a system to access large-scale open data from LHCb