Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks

• URL: http://arxiv.org/abs/2008.02528v1
• Date: Thu, 6 Aug 2020 09:02:02 GMT
• Title: Learning Sampling in Financial Statement Audits using Vector Quantised Autoencoder Neural Networks
• Authors: Marco Schreyer, Timur Sattarov, Anita Gierbl, Bernd Reimer and Damian Borth
• Abstract summary: We propose the application of Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data.
• Score: 1.2205797997133396
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: The audit of financial statements is designed to collect reasonable assurance that an issued statement is free from material misstatement 'true and fair presentation'. International audit standards require the assessment of a statements' underlying accounting relevant transactions referred to as 'journal entries' to detect potential misstatements. To efficiently audit the increasing quantities of such entries, auditors regularly conduct a sample-based assessment referred to as 'audit sampling'. However, the task of audit sampling is often conducted early in the overall audit process. Often at a stage, in which an auditor might be unaware of all generative factors and their dynamics that resulted in the journal entries in-scope of the audit. To overcome this challenge, we propose the application of Vector Quantised-Variational Autoencoder (VQ-VAE) neural networks. We demonstrate, based on two real-world city payment datasets, that such artificial neural networks are capable of learning a quantised representation of accounting data. We show that the learned quantisation uncovers (i) the latent factors of variation and (ii) can be utilised as a highly representative audit sample in financial statement audits.

Related papers

• Auditing for Bias in Ad Delivery Using Inferred Demographic Attributes [50.37313459134418]
  We study the effects of inference error on auditing for bias in one prominent application: black-box audit of ad delivery using paired ads. We propose a way to mitigate the inference error when evaluating skew in ad delivery algorithms.
  arXiv  Detail & Related papers  (2024-10-30T18:57:03Z)
• AuditWen:An Open-Source Large Language Model for Audit [20.173039073935907]
  This study introduces AuditWen, an open-source audit LLM by fine-tuning Qwen with constructing instruction data from audit domain. We propose an audit LLM, called AuditWen, by fine-tuning Qwen with constructing 28k instruction dataset from 15 audit tasks and 3 layers. In evaluation stage, we proposed a benchmark with 3k instructions that covers a set of critical audit tasks derived from the application scenarios. The experimental results demonstrate superior performance of AuditWen both in question understanding and answer generation, making it an immediately valuable tool for audit.
  arXiv  Detail & Related papers  (2024-10-09T02:28:55Z)
• Advancing Anomaly Detection: Non-Semantic Financial Data Encoding with LLMs [49.57641083688934]
  We introduce a novel approach to anomaly detection in financial data using Large Language Models (LLMs) embeddings. Our experiments demonstrate that LLMs contribute valuable information to anomaly detection as our models outperform the baselines.
  arXiv  Detail & Related papers  (2024-06-05T20:19:09Z)
• The Decisive Power of Indecision: Low-Variance Risk-Limiting Audits and Election Contestation via Marginal Mark Recording [51.82772358241505]
  Risk-limiting audits (RLAs) are techniques for verifying the outcomes of large elections. We define new families of audits that improve efficiency and offer advances in statistical power. New audits are enabled by revisiting the standard notion of a cast-vote record so that it can declare multiple possible mark interpretations.
  arXiv  Detail & Related papers  (2024-02-09T16:23:54Z)
• From Chaos to Clarity: Claim Normalization to Empower Fact-Checking [57.024192702939736]
  Claim Normalization (aka ClaimNorm) aims to decompose complex and noisy social media posts into more straightforward and understandable forms. We propose CACN, a pioneering approach that leverages chain-of-thought and claim check-worthiness estimation. Our experiments demonstrate that CACN outperforms several baselines across various evaluation measures.
  arXiv  Detail & Related papers  (2023-10-22T16:07:06Z)
• Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory [55.878249096379804]
  We study different ways to categorize according to different extents of interest in different financial accounts. The framework developed in this paper provides a formal ground to obtain and study explainable categorizations.
  arXiv  Detail & Related papers  (2022-10-31T13:49:16Z)
• Federated Continual Learning to Detect Accounting Anomalies in Financial Auditing [1.2205797997133396]
  We propose a Federated Continual Learning framework enabling auditors to learn audit models from decentral clients continuously. We evaluate the framework's ability to detect accounting anomalies in common scenarios of organizational activity.
  arXiv  Detail & Related papers  (2022-10-26T21:33:08Z)
• RESHAPE: Explaining Accounting Anomalies in Financial Statement Audits by enhancing SHapley Additive exPlanations [1.3333957453318743]
  We propose (RESHAPE) which explains the model output on an aggregated attribute-level. Our 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.
  arXiv  Detail & Related papers  (2022-09-19T16:23:43Z)
• Algorithmic Fairness and Vertical Equity: Income Fairness with IRS Tax Audit Models [73.24381010980606]
  This study examines issues of algorithmic fairness in the context of systems that inform tax audit selection by the IRS. We show how the use of more flexible machine learning methods for selecting audits may affect vertical equity. Our results have implications for the design of algorithmic tools across the public sector.
  arXiv  Detail & Related papers  (2022-06-20T16:27:06Z)
• Continual Learning for Unsupervised Anomaly Detection in Continuous Auditing of Financial Accounting Data [1.9659095632676094]
  International audit standards require the direct assessment of a financial statement's underlying accounting journal entries. Deep-learning inspired audit techniques emerged to examine vast quantities of journal entry data. This work proposes a continual anomaly detection framework to overcome both challenges and designed to learn from a stream of journal entry data experiences.
  arXiv  Detail & Related papers  (2021-12-25T09:21:14Z)
• Multi-view Contrastive Self-Supervised Learning of Accounting Data Representations for Downstream Audit Tasks [1.9659095632676094]
  International audit standards require the direct assessment of a financial statement's underlying accounting transactions, referred to as journal entries. Deep learning inspired audit techniques have emerged in the field of auditing vast quantities of journal entry data. We propose a contrastive self-supervised learning framework designed to learn audit task invariant accounting data representations.
  arXiv  Detail & Related papers  (2021-09-23T08:16:31Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.