Towards Automated Auditing with Machine Learning
Rafet Sifa, Anna Ladi, Maren Pielka, Rajkumar Ramamurthy, Lars Hillebrand, Birgit Kirsch, David Biesner, Robin Stenzel, Thiago Bell, Max Lübbering, Ulrich Nütten, Christian Bauckhage, Ulrich Warning, Benedikt Fürst, Tim Dilmaghani Khameneh, Daniel Thom, Ilgar Huseynov, Roland Kahlert, Jennifer Schlums, Hisham Ismail · 2019
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.