Detection of Abuse in Financial Transaction Descriptions Using Machine Learning
Not on the map. Why?
Abstract (arXiv)
Since introducing changes to the New Payments Platform (NPP) to include longer messages as payment descriptions, it has been identified that people are now using it for communication, and in some cases, the system was being used as a targeted form of domestic and family violence. This type of tech-assisted abuse poses new challenges in terms of identification, actions and approaches to rectify this behaviour. Commonwealth Bank of Australia's Artificial Intelligence Labs team (CBA AI Labs) has developed a new system using advances in deep learning models for natural language processing (NLP) to create a powerful abuse detector that periodically scores all the transactions, and identifies cases of high-risk abuse in millions of records. In this paper, we describe the problem of tech-assisted abuse in the context of banking services, outline the developed model and its performance, and the operating framework more broadly.
Key facts. No citing papers in the map yet (data through 7 Oct 2026). Builds on 7 of its 28 references that are in the map. First posted on arXiv 10 Mar 2023.
Builds on
7 of 28 references.
References · 7
- 2018BERT: Pre-training of Deep Bidirectional Transformers for Language Understandingcited by 40,204
- 2019RoBERTa: A Robustly Optimized BERT Pretraining Approachcited by 13,266
- 2019SemEval-2019 Task 6: Identifying and Categorizing Offensive Language in Social Media (OffensEval)cited by 206
- 2021Entailment as Few-Shot Learnercited by 122
- 2017"Like Sheep Among Wolves": Characterizing Hateful Users on Twittercited by 8
- 2017Measuring #GamerGate: A Tale of Hate, Sexism, and Bullyingcited by 7
- 2019Neural Word Decomposition Models for Abusive Language Detectioncited by 6
Built on by
Not cited yet.