Summary of Flexible Categorization For Auditing Using Formal Concept Analysis and Dempster-shafer Theory, by Marcel Boersma et al.
Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory
by Marcel Boersma, Krishna Manoorkar, Alessandra Palmigiano, Mattia Panettiere, Apostolos Tzimoulis, Nachoem Wijnberg
First submitted to arxiv on: 31 Oct 2022
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: Logic in Computer Science (cs.LO)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This research paper presents a novel approach to auditing by developing a framework for categorizing business processes based on weighted bipartite graphs representing transactions between financial accounts. The authors view these graphs as many-valued formal contexts, which are then used to obtain explainable categorizations of business processes using methods from formal concept analysis. The proposed methodology provides several advantages over non-explainable machine learning techniques and can be taken as a basis for developing algorithms that perform clustering on transparent and accountable principles. The paper focuses on obtaining and studying different ways to categorize according to different extents of interest in different financial accounts or interrogative agendas of various agents or sub-tasks in audit. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Business processes are crucial in auditing, but large amounts of data can be overwhelming. This research helps by creating a way to organize these processes using special graphs and rules from math. The goal is to understand how different people with different interests might group certain financial transactions together. The study shows how to get this done and even includes scenarios where people discuss and agree on the categories. |
Keywords
» Artificial intelligence » Clustering » Machine learning
