This work aims to improve the quality of Internal Audits (IA) that are a critical part of an organization's governance structure and serves as third line of defense helping provide assurance that the controls and processes have adequate risk mitigation strategies in place. We focus on AI enabled internal audits that could improve the quality, coverage and time needed to perform them and thus improve the efectiveness and eficiency of providing assurance, to help auditors identify potential risks that may go unnoticed through traditional methods. We compare diferent AI methodologies that can be used in controls testing for various financial and corporate processes. We propose the use of Knowledge Graphs (KGs) and representational learning to leverage the inherent relational nature of the data and to identify potential non-compliance or fraud. The experimental results demonstrate that our proposed method exhibits a significant improvement in F1 score, outperforming standard outlier detection approaches, reducing the number of False Positives (FPs) and in turn the manual review involved.
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© 2022 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0). we identify relationships between the columns, and model the data as a KG. The KG Schema has five primary nodes in red, as shown in the Fig 1, each of which are described by their respective attributes in green (e.g. transaction amount for the transaction identifier node). The dataset contains continuous, discrete and textual columns, each of which are incorporated in the graph with appropriate pre-processing steps: Continuous values are binned, textual attributes are cleaned, split into keywords and semantically similar words are connected using Bidirectional Encoder Representations from Transformers (BERT)-based word embeddings. We then use a semi-supervised setup, where a small fraction of transactions (< 1%) are noisy labelled as fraudulent based on a small set of controls and are assigned an edge in the KG.
KGE
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