Using Autoencoders for Data-Driven Analysis in Internal Auditing

Nonnenmacher, Jakob; Kruse, Felix; Schumann, Gerrit; Gómez, Jorge Marx

doi:10.24251/hicss.2021.697

Cited by 16 publications

(13 citation statements)

References 39 publications

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“…Usage of auto coders ( a technique based on AI) as an unsupervised method evaluated in auditing in a practical case study showed that auto coders can support the auditors in the audit execution and audit planning processes to improve the quality of internal audit engagement [27].…”

Section: Review Of Related Literaturementioning

confidence: 99%

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

Puthukulam¹,

Ravikumar²,

Sharma³

et al. 2021

ujaf

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Section: Review Of Related Literaturementioning

confidence: 99%

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

Puthukulam¹,

Ravikumar²,

Sharma³

et al. 2021

ujaf

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“…Furthermore, Zupan et al [54] used Long Short-Term Memory AAENs to detect temporal anomalies in journal entry data. In addition, Nonnenmacher et al [32] and Schreyer et al [40] demonstrated that AENNs could be used to improve audit sampling during an audit process. Furthermore, it was shown that AENNs can be trained in a self-supervised learning setup to detect accounting anomalies and complete additional down-stream audit tasks [38].…”

Section: Detection Of Accounting Anomaliesmentioning

confidence: 99%

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Müller¹,

Schreyer²,

Sattarov³

et al. 2022

Preprint

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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 Reconstruction Error SHapley Additive exPlanations Extension (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. CCS CONCEPTS• Computing methodologies → Machine learning; • Information systems → Enterprise resource planning.

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“…Such methods rely on 'endto-end' machine-learned accounting data representations rather than human-engineered features. Such techniques encompass: (i) autoencoder neural networks [43,48,52], (ii) variational autoencoders [66], and (iii) adversarial autoencoders [50]. Lately, vector quantized variational autoencoders have been applied to learn representative audit sampling [49].…”

Section: Accounting Data Representation Learningmentioning

confidence: 99%

Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks

Schreyer

Sattarov

Borth

2021

Proceedings of the Second ACM International Conference on AI in Finance

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International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries. Recently, driven by the advances in artificial intelligence, deep learning inspired audit techniques have emerged in the field of auditing vast quantities of journal entry data. Nowadays, the majority of such methods rely on a set of specialized models, each trained for a particular audit task. At the same time, when conducting a financial statement audit, audit teams are confronted with (i) challenging time-budget constraints, (ii) extensive documentation obligations, and (iii) strict model interpretability requirements. As a result, auditors prefer to harness only a single preferably 'multi-purpose' model throughout an audit engagement. We propose a contrastive self-supervised learning framework designed to learn audit task invariant accounting data representations to meet this requirement. The framework encompasses deliberate interacting data augmentation policies that utilize the attribute characteristics of journal entry data. We evaluate the framework on two real-world datasets of city payments and transfer the learned representations to three downstream audit tasks: anomaly detection, audit sampling, and audit documentation. Our experimental results provide empirical evidence that the proposed framework offers the ability to increase the efficiency of audits by learning rich and interpretable 'multi-task' representations.

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Using Autoencoders for Data-Driven Analysis in Internal Auditing

Cited by 16 publications

References 39 publications

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

Auditors' Perception on the Impact of Artificial Intelligence on Professional Skepticism and Judgment in Oman

RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations

Multi-view contrastive self-supervised learning of accounting data representations for downstream audit tasks

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