mlsanityData to 14 Oct 2026

Towards Artificial Intelligence Enabled Financial Crime Detection

arXiv 2105.10866 ↗First posted 23 May 2021cs.LG cs.IR q-fin.STcited by 1

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Abstract (arXiv)

Recently, financial institutes have been dealing with an increase in financial crimes. In this context, financial services firms started to improve their vigilance and use new technologies and approaches to identify and predict financial fraud and crime possibilities. This task is challenging as institutions need to upgrade their data and analytics capabilities to enable new technologies such as Artificial Intelligence (AI) to predict and detect financial crimes. In this paper, we put a step towards AI-enabled financial crime detection in general and money laundering detection in particular to address this challenge. We study and analyse the recent works done in financial crime detection and present a novel model to detect money laundering cases with minimum human intervention needs.

Key facts. Cited by 1 paper in the map (data through 14 Oct 2026). Builds on 10 of its 100 references that are in the map. First posted on arXiv 23 May 2021.

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10 of 100 references.

References · 10

  1. 2017Snorkel: Rapid Training Data Creation with Weak Supervisioncited by 372
  2. 2019Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensicscited by 164
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  8. 2017Predicting Financial Crime: Augmenting the Predictive Policing Arsenalcited by 2
  9. 2019Community-Level Anomaly Detection for Anti-Money Launderingcited by 2
  10. 2016A data mining-based solution for detecting suspicious money laundering cases in an investment bankcited by 1

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Citing papers per year: 2023: 1.

1 paper.

  1. 2023A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detectioncited by 10