Towards Automated Auditing with Machine Learning
| Autor: | Anna Ladi, Thiago Bell, Ilgar Huseynov, Rajkumar Ramamurthy, Lars Patrick Hillebrand, Benedikt Fürst, Birgit Kirsch, Daniel Thom, Max Lübbering, Christian Bauckhage, David Biesner, Hisham Ismail, Bernd Kliem, Maren Pielka, Tim Dilmaghani Khameneh, Roland Kahlert, Jennifer Schlums, Ulrich Nütten, Rüdiger Loitz, Ulrich Warning, Robin Stenzel, Rafet Sifa |
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| Rok vydání: | 2019 |
| Předmět: |
Computer science
business.industry 05 social sciences 050201 accounting 02 engineering and technology Audit Recommender system Machine learning computer.software_genre Business process management Subject-matter expert Recurrent neural network Binary classification 0502 economics and business 0202 electrical engineering electronic engineering information engineering 020201 artificial intelligence & image processing Language model Artificial intelligence business computer Financial statement |
| Zdroj: | DocEng |
| DOI: | 10.1145/3342558.3345421 |
| Popis: | We present the Automated List Inspection (ALI) tool that utilizes methods from machine learning, natural language processing, combined with domain expert knowledge to automate financial statement auditing. ALI is a content based context-aware recommender system, that matches relevant text passages from the notes to the financial statement to specific law regulations. In this paper, we present the architecture of the recommender tool which includes text mining, language modeling, unsupervised and supervised methods that range from binary classification models to deep recurrent neural networks. Next to our main findings, we present quantitative and qualitative comparisons of the algorithms as well as concepts for how to further extend the functionality of the tool. |
| Databáze: | OpenAIRE |
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