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Applying Natural Language Processing to Financial Risk Disclosures and Audit Trails

Author(s) Prashant Singh
Country United States
Abstract In a space such as the financial industry, clear and stringent reporting and auditing are vital for both regulatory adherence and internal governance. In this environment, cards with full U.S cardholder's CVVs are one of the most valuable data assets for a fraudster in that they provide an easy-to-use, one-time authentication step that’s cryptographically difficult to reproduce. Although these information resources are invaluable for evaluating institutions' risk and compliance stance, the vast majority of such information is textual and unstructured. This becomes a formidable challenge for institutions that try to make use of timely, reliable, and actionable insights- especially when they are done manually or with unsophisticated rule-based systems.
In the past few years, the developments in NLP have provided a tremendous ability to interpret unstructured text at scale, enabling automation in areas that traditionally rely heavily on expert judgment. NLP is particularly suitable in finance applications where Textual analysis is required to deal with context, domain-specific jargon, time taking into consideration temporal patterns, and delicate linguistic cues. This work studied NLP to process financial risk disclosures and audit trails, providing a systematic and scalable way to detect financial wrongdoings, latent risks, and non-compliance events.
We start with an analysis of the linguistic properties of financial disclosures, uncovering important aspects such as tone, modality, and forward-looking statements that are frequently associated with risk perception and market volatility. We leverage techniques such as Named Entity Recognition (NER), sentiment analysis, and topic modelling to illustrate how machine learning-based NLP models can unearth the hidden risk signals encoded in annual reports or regulatory filings. Concurrently, we consider audit trails as structured logs about user or system activity that, despite being in timestamped format, include embedded command-line lines, transactional notes, and system-generated messages that are good candidates for language-based analysis. By processing through NLP, such as tokenization of log, part-of-speech, parsing, and anomaly detection, the audit data is converted to the sample structured knowledge for real-time monitoring and forensic auditing.
The manuscript introduces a hybrid approach based on the integration of rule-based, statistical NLP, and machine-learning techniques for both narrative-based disclosures and event-ordered disclosure logs. We also detail a pipeline design consisting of data ingestion, text pre-processing, feature extraction, model prediction, and visual dashboarding. Experimental results from historical financial disclosures and synthetic audit logs show that the NLP-driven framework is able to accurately target risk-laden statements, identify anomalous sequences of activities, and categorize text sections according to regulatory relevance. Our results show that our proposed approach outperforms traditional keyword matching and manual review-based approaches and is more efficient and interpretable.
The application of NLP to financial risk risk disclosures and audit trails can improve both timeliness and accuracy of compliance checks while also providing a proactive approach to risk governance. This study is part of an emerging body of work on Regulatory Technology (RegTech), which promotes the use of AI and data to inform regulatory decision making in finance. In navigating the morass of regulation and the volume of data they need to process, it is clear that NLP is the key enabler for intelligent, automated, and reliable compliance.
Field Engineering
Published In Volume 14, Issue 1, January-June 2023
Published On 2023-01-05
Cite This Applying Natural Language Processing to Financial Risk Disclosures and Audit Trails - Prashant Singh - IJAIDR Volume 14, Issue 1, January-June 2023. DOI 10.71097/IJAIDR.v14.i1.1446
DOI https://doi.org/10.71097/IJAIDR.v14.i1.1446
Short DOI https://doi.org/g9q34v
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