631 research outputs found
GAN Augmented Text Anomaly Detection with Sequences of Deep Statistics
Anomaly detection is the process of finding data points that deviate from a
baseline. In a real-life setting, anomalies are usually unknown or extremely
rare. Moreover, the detection must be accomplished in a timely manner or the
risk of corrupting the system might grow exponentially. In this work, we
propose a two level framework for detecting anomalies in sequences of discrete
elements. First, we assess whether we can obtain enough information from the
statistics collected from the discriminator's layers to discriminate between
out of distribution and in distribution samples. We then build an unsupervised
anomaly detection module based on these statistics. As to augment the data and
keep track of classes of known data, we lean toward a semi-supervised
adversarial learning applied to discrete elements.Comment: 5 pages, 53rd Annual Conference on Information Sciences and Systems,
CISS 201
Explaining Anomalies using Denoising Autoencoders for Financial Tabular Data
Recent advances in Explainable AI (XAI) increased the demand for deployment
of safe and interpretable AI models in various industry sectors. Despite the
latest success of deep neural networks in a variety of domains, understanding
the decision-making process of such complex models still remains a challenging
task for domain experts. Especially in the financial domain, merely pointing to
an anomaly composed of often hundreds of mixed type columns, has limited value
for experts. Hence, in this paper, we propose a framework for explaining
anomalies using denoising autoencoders designed for mixed type tabular data. We
specifically focus our technique on anomalies that are erroneous observations.
This is achieved by localizing individual sample columns (cells) with potential
errors and assigning corresponding confidence scores. In addition, the model
provides the expected cell value estimates to fix the errors. We evaluate our
approach based on three standard public tabular datasets (Credit Default,
Adult, IEEE Fraud) and one proprietary dataset (Holdings). We find that
denoising autoencoders applied to this task already outperform other approaches
in the cell error detection rates as well as in the expected value rates.
Additionally, we analyze how a specialized loss designed for cell error
detection can further improve these metrics. Our framework is designed for a
domain expert to understand abnormal characteristics of an anomaly, as well as
to improve in-house data quality management processes.Comment: 10 pages, 4 figures, 3 tables, preprint versio
RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations
Detecting accounting anomalies is a recurrent challenge in financial
statement audits. Recently, novel methods derived from Deep-Learning (DL) have
been proposed to audit the large volumes of a statement's underlying accounting
records. However, due to their vast number of parameters, such models exhibit
the drawback of being inherently opaque. At the same time, the concealing of a
model's inner workings often hinders its real-world application. This
observation holds particularly true in financial audits since auditors must
reasonably explain and justify their audit decisions. Nowadays, various
Explainable AI (XAI) techniques have been proposed to address this challenge,
e.g., SHapley Additive exPlanations (SHAP). However, in unsupervised DL as
often applied in financial audits, these methods explain the model output at
the level of encoded variables. As a result, the explanations of Autoencoder
Neural Networks (AENNs) are often hard to comprehend by human auditors. To
mitigate this drawback, we propose (RESHAPE), which explains the model output
on an aggregated attribute-level. In addition, we introduce an evaluation
framework to compare the versatility of XAI methods in auditing. Our
experimental results show empirical evidence that RESHAPE results in versatile
explanations compared to state-of-the-art baselines. We envision such
attribute-level explanations as a necessary next step in the adoption of
unsupervised DL techniques in financial auditing.Comment: 9 pages, 4 figures, 5 tables, preprint version, currently under
revie
Quick survey of graph-based fraud detection methods
In general, anomaly detection is the problem of distinguishing between normal
data samples with well defined patterns or signatures and those that do not
conform to the expected profiles. Financial transactions, customer reviews,
social media posts are all characterized by relational information. In these
networks, fraudulent behaviour may appear as a distinctive graph edge, such as
spam message, a node or a larger subgraph structure, such as when a group of
clients engage in money laundering schemes. Most commonly, these networks are
represented as attributed graphs, with numerical features complementing
relational information. We present a survey on anomaly detection techniques
used for fraud detection that exploit both the graph structure underlying the
data and the contextual information contained in the attributes
The New Abnormal: Network Anomalies in the AI Era
Anomaly detection aims at finding unexpected patterns in data. It has been used in several problems in computer networks, from the detection of port scans and DDoS attacks to the monitoring of time-series collected from Internet monitoring systems. Data-driven approaches and machine learning have seen widespread application on anomaly detection too, and this trend has been accelerated by the recent developments on Artificial Intelligence research. This chapter summarizes ongoing recent progresses on anomaly detection research. In particular, we evaluate how developments on AI algorithms bring new possibilities for anomaly detection. We cover new representation learning techniques such as Generative Artificial Networks and Autoencoders, as well as techniques that can be used to improve models learned with machine learning algorithms, such as reinforcement learning. We survey both research works and tools implementing AI algorithms for anomaly detection. We found that the novel algorithms, while successful in other fields, have hardly been applied to networking problems. We conclude the chapter with a case study that illustrates a possible research direction
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
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