Learning to detect fraud in large-scale accounting data is one of the long-standing challenges in financial statement audits or fraud investigations. Nowadays, the majority of applied techniques refer to handcrafted rules derived from known fraud scenarios. While fairly successful, these rules exhibit the drawback that they often fail to generalize beyond known fraud scenarios and fraudsters gradually find ways to circumvent them. To overcome this disadvantage and inspired by the recent success of deep learning we propose the application of deep autoencoder neural networks to detect anomalous journal entries. We demonstrate that the trained network's reconstruction error obtainable for a journal entry and regularized by the entry's individual attribute probabilities can be interpreted as a highly adaptive anomaly assessment. Experiments on two real-world datasets of journal entries, show the effectiveness of the approach resulting in high f1-scores of 32.93 (dataset A) and 16.95 (dataset B) and less false positive alerts compared to state of the art baseline methods. Initial feedback received by chartered accountants and fraud examiners underpinned the quality of the approach in capturing highly relevant accounting anomalies.
Concluding from the reviewed literature, the majority of references draw either (1) on historical accounting and forensic knowledge about various "red-flags" and fraud schemes or (2) on traditional non-deep learning techniques. As a result and in agreement with @cite_36 , we see a demand for unsupervised and novel approaches capable to detect so far unknown scenarios of fraudulent journal entries.
@cite_36
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Most previous studies in computerized deception detection have relied only on shallow lexico-syntactic patterns. This paper investigates syntactic stylometry for deception detection, adding a somewhat unconventional angle to prior literature. Over four different datasets spanning from the product review to the essay domain, we demonstrate that features driven from Context Free Grammar (CFG) parse trees consistently improve the detection performance over several baselines that are based only on shallow lexico-syntactic features. Our results improve the best published result on the hotel review data (, 2011) reaching 91.2 accuracy with 14 error reduction. This survey paper categorizes, compares, and summarizes the data set, algorithm and performance measurement in almost all published technical and review articles in automated accounting fraud detection. Most researches regard fraud companies and non-fraud companies as data subjects, Eigenvalue covers auditor data, company governance data, financial statement data, industries, trading data and other categories. Most data in earlier research were auditor data; Later research establish model by using sharing data and public statement data. Company governance data have been widely used. It is generally believed that ratio data is more effective than accounting data; Seldom research on time Series Data Mining were conducted. The retrieved literature used mining algorithms including statistical test, regression analysis, neural networks, decision tree, Bayesian network, and stack variables etc.. Regression Analysis is widely used on hiding data. Generally the detecting effect and accuracy of NN are superior to regression model. General conclusion is that model detecting is better than auditor detecting rate without assisting. There is a need to introduce other algorithms of no-tag data mining. Owing to the small size of fraud samples, some literature reached conclusion based on training samples and may overestimated the effect of model. In recent years, there have been several large accounting frauds where a company's financial results have been intentionally misrepresented by billions of dollars. In response, regulatory bodies have mandated that auditors perform analytics on detailed financial data with the intent of discovering such misstatements. For a large auditing firm, this may mean analyzing millions of records from thousands of clients. This paper proposes techniques for automatic analysis of company general ledgers on such a large scale, identifying irregularities - which may indicate fraud or just honest errors - for additional review by auditors. These techniques have been implemented in a prototype system, called Sherlock, which combines aspects of both outlier detection and classification. In developing Sherlock, we faced three major challenges: developing an efficient process for obtaining data from many heterogeneous sources, training classifiers with only positive and unlabeled examples, and presenting information to auditors in an easily interpretable manner. In this paper, we describe how we addressed these challenges over the past two years and report on experiments evaluating Sherlock. When creating a forgery, a forger can modify an image using many different image editing operations. Since a forensic examiner must test for each of these, significant interest has arisen in the development of universal forensic algorithms capable of detecting many different image editing operations and manipulations. In this paper, we propose a universal forensic approach to performing manipulation detection using deep learning. Specifically, we propose a new convolutional network architecture capable of automatically learning manipulation detection features directly from training data. In their current form, convolutional neural networks will learn features that capture an image's content as opposed to manipulation detection features. To overcome this issue, we develop a new form of convolutional layer that is specifically designed to suppress an image's content and adaptively learn manipulation detection features. Through a series of experiments, we demonstrate that our proposed approach can automatically learn how to detect multiple image manipulations without relying on pre-selected features or any preprocessing. The results of these experiments show that our proposed approach can automatically detect several different manipulations with an average accuracy of 99.10 .