Categorization of business processes is an important part of auditing. Large amounts of transactional 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. We use Dempster-Shafer mass functions to represent agendas showing different interest in different set of financial accounts. 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. We use this framework to describe a machine-leaning meta algorithm for outlier detection and classification which can provide local and global explanations of its result and demonstrate it through an outlier detection algorithm.

翻译：业务流程分类是审计中的重要环节。审计中大量的交易数据可表示为加权二分图，用以刻画财务账户间的交易关系。我们将此类二分图视为多值形式背景，通过形式概念分析方法，利用业务流程所涉及的财务账户对其进行可解释性分类。采用Dempster-Shafer质量函数来表征对不同财务账户集合具有不同关注度的议程。我们还模拟了具有不同质询议程的智能体之间通过协商达成整合议程与分类的若干可能场景。本文提出的框架为从二分图数据中根据组织（如审计事务所）内不同智能体的议程及其通过协商产生的交互作用，获取并分析可解释性分类提供了形式化基础。基于该框架，我们描述了一种用于异常检测与分类的机器学习元算法，该算法可对其结果提供局部与全局解释，并通过异常检测算法进行了验证。