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Towards Automated Auditing with Machine Learning

 
: Sifa, Rafet; Ladi, Anna; Pielka, Maren; Ramamurthy, Rajkumar; Hillebrand, Lars; Kirsch, Birgit; Biesner, David; Stenzel, R.; Bell, T.; Lübbering, M.; Nütten, U.; Bauckhage, C.; Warning, U.; Fürst, B.; Khameneh, T.D.; Thom, D.; Huseynov, I.; Kahlert, R.; Schlums, J.; Ismail, H.; Kliem, B.; Loitz, R.

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Borghoff, U. ; Association for Computing Machinery -ACM-:
DocEng 2019, 19th ACM Symposium on Document Engineering. Proceedings : September 23-26, 2019, Berlin, Germany
New York: ACM, 2019
ISBN: 978-1-4503-6887-2
Art. 41, 4 pp.
Symposium on Document Engineering (DocEng) <19, 2019, Berlin>
English
Conference Paper
Fraunhofer IAIS ()
text mining; business process optimization; automated auditing

Abstract
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.

: http://publica.fraunhofer.de/documents/N-593327.html