# Client Profiling for an Anti-Money Laundering System

arXiv: https://arxiv.org/abs/1510.00878 · HTML: https://mlsanity.com/paper/1510.00878/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 3 Oct 2015.

## Built on by (most cited first, 4 of 4)

- [Fighting Money Laundering with Statistics and Machine Learning](https://mlsanity.com/paper/2201.04207/) (2022)
- [Combating Financial Crimes with Unsupervised Learning Techniques: Clustering and Dimensionality Reduction for Anti-Money Laundering](https://mlsanity.com/paper/2403.00777/) (2024)
- [Anti-Money Laundering Systems Using Deep Learning](https://mlsanity.com/paper/2509.19359/) (2025)
- [AI Application in Anti-Money Laundering for Sustainable and Transparent Financial Systems](https://mlsanity.com/paper/2512.06240/) (2025)

## Abstract (arXiv)

We present a data mining approach for profiling bank clients in order to support the process of detection of anti-money laundering operations. We first present the overall system architecture, and then focus on the relevant component for this paper. We detail the experiments performed on real world data from a financial institution, which allowed us to group clients in clusters and then generate a set of classification rules. We discuss the relevance of the founded client profiles and of the generated classification rules. According to the defined overall agent-based architecture, these rules will be incorporated in the knowledge base of the intelligent agents responsible for the signaling of suspicious transactions.