The goal of the research cooperation DIfA (Data Intelligence for Audit) is to develop data-driven methods and approaches for internal auditing, which enable the identification of process weaknesses, inconsistencies, manipulations and fraud in business processes. With this the transition from a sampling based audit approach to a full population testing is supported and it is shown how new data science approaches can be applied within an internal auditing context.
Since internal auditing is considering both structured data (e.g. database-tables) as well as unstructured data (e.g. pdf-documents), the approach to address the research goal is broken down into the hypothesis-free analysis of structured data as well as natural language processing for the analysis of unstructured data.
The research project DIfA is realized in cooperation with Volkswagen AG and is set to take three years. During this time students will have the opportunity to write bachelor’s and master’s theses within the project.
- Hypothesis-free analysis of structured data
- Natural Language Processing for analyzing unstructured data
Volkswagen AG (Wolfsburg)