A framework for anomaly detection using language modeling, and its applications to finance
This work addresses anomaly detection for the finance sector, but it is incremental as it adapts existing language modeling methods to a new domain without introducing a novel paradigm.
The paper tackles the problem of anomaly detection in financial text corpora by proposing a framework that uses language models with distributional semantics, aiming to enable new applications in risk identification, predictive modeling, and trend analysis.
In the finance sector, studies focused on anomaly detection are often associated with time-series and transactional data analytics. In this paper, we lay out the opportunities for applying anomaly and deviation detection methods to text corpora and challenges associated with them. We argue that language models that use distributional semantics can play a significant role in advancing these studies in novel directions, with new applications in risk identification, predictive modeling, and trend analysis.