Sushodhan Vaishampayan

Financial audits establish trust in the governance and processes in an organization, but they are time-consuming and knowledge intensive. To increase the effectiveness of financial audit, we address the task of generating audit suggestions that can help auditors to focus their investigations. Specifically, we present NLP techniques to extract hidden knowledge from a corpus of past financial audit reports of many companies, and use it for generating audit suggestions. The extracted knowledge consists of a set of automatically identified sentences containing adverse remarks, the financial variables mentioned in each sentence and automatically assigned XBRL categories for them, since XBRL is a standardized taxonomy in the financial domain. In the absence of suitable labeled data, we adopted a weak supervision approach. We designed a set of high precision linguistic rules to identify adverse remark sentences, created automatically labeled training data using them, and trained BERT-based and other classifiers to identify such sentences. We next presented novel techniques (which are either unsupervised or zero-shot) to assign zero, one, or more XBRL categories to any given adverse remark sentence. We evaluated the proposed approaches, on a large corpus of real-life financial statements and audit reports, against competent baselines. Given a company’s financial statements (already identified as suspicious), and given a subset of financial variables in them that contribute to suspiciousness, we match these with the extracted knowledge base and identify aligned adverse remarks that help the auditor in focusing on specific directions for further investigations.

This paper explains how the traditional anomaly detection techniques lack in exploring various aspects and perspectives of data analysis and lacks in understanding the nature of anomalies. This paper explains the importance and need of developing anomaly detection techniques considering the domain knowledge of the field where the anomaly detection technique is to be applied. Here we have considered the notion of causality(Granger's Causality[10]) in data to perform anomaly detection. This paper states some examples supporting the need of domain specific anomaly detection techniques. It also explains a few aspects which have not been considered in anomaly detection techniques till date.