Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory

Categorization of business processes is an important part of auditing. Large amounts of transnational data in auditing can be represented as transactions between financial accounts using weighted bipartite graphs. We view such bipartite graphs as many-valued formal contexts, which we use to obtain explainable categorization of these business processes in terms of financial accounts involved in a business process by using methods in formal concept analysis. The specific explainability feature of the methodology introduced in the present paper provides several advantages over e.g. non-explainable machine learning techniques, and in fact, it can be taken as a basis for the development of algorithms which perform the task of clustering on transparent and accountable principles. Here, we focus on obtaining and studying di ff erent ways to categorize according to di ff erent extents of interest in di ff erent financial accounts, or interrogative agendas , of various agents or sub-tasks in audit. We use Dempster-Shafer mass functions to represent agendas showing di ff erent interest in di ff erent set of financial accounts. We propose two new methods to obtain categorizations from these agendas. We also model some possible deliberation scenarios between agents with di ff erent interrogative agendas to reach an aggregated agenda and categorization. The framework developed in this paper provides a formal ground to obtain and study explainable categorizations from the data represented as bipartite graphs according to the agendas of di ff erent agents in an organization (e.g. an audit firm), and interaction between these through deliberation.