Flexible categorization for auditing using formal concept analysis and Dempster-Shafer theory
This provides a formal framework for explainable categorization in auditing, addressing transparency and accountability issues compared to non-explainable machine learning techniques, though it is incremental as it builds on existing methods like formal concept analysis and Dempster-Shafer theory.
The paper tackles the problem of categorizing business processes in auditing by representing transactional data as bipartite graphs and using formal concept analysis to obtain explainable categorizations based on financial accounts, with results including two new methods for categorization from Dempster-Shafer mass functions and modeling deliberation scenarios between agents.
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 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. We use Dempster-Shafer mass functions to represent agendas showing different interest in different 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 different 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 different agents in an organization (e.g.~an audit firm), and interaction between these through deliberation.