# Towards Artificial Intelligence Enabled Financial Crime Detection

arXiv: https://arxiv.org/abs/2105.10866 · HTML: https://mlsanity.com/paper/2105.10866/

## Key facts

- Cited by 1 papers 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.

## Builds on (most cited first, 10 of 10)

- [Snorkel: Rapid Training Data Creation with Weak Supervision](https://mlsanity.com/paper/1711.10160/) (2017)
- [Anti-Money Laundering in Bitcoin: Experimenting with Graph Convolutional Networks for Financial Forensics](https://mlsanity.com/paper/1908.02591/) (2019)
- [HoloClean: Holistic Data Repairs with Probabilistic Inference](https://mlsanity.com/paper/1702.00820/) (2017)
- [Enabling the Analysis of Personality Aspects in Recommender Systems](https://mlsanity.com/paper/2001.04825/) (2020)
- [Relational Learning Analysis of Social Politics using Knowledge Graph Embedding](https://mlsanity.com/paper/2006.01626/) (2020)
- [Detection of money laundering groups using supervised learning in networks](https://mlsanity.com/paper/1608.00708/) (2016)
- [Comprehensive and Reliable Crowd Assessment Algorithms](https://mlsanity.com/paper/1411.3377/) (2014)
- [Predicting Financial Crime: Augmenting the Predictive Policing Arsenal](https://mlsanity.com/paper/1704.07826/) (2017)
- [Community-Level Anomaly Detection for Anti-Money Laundering](https://mlsanity.com/paper/1910.11313/) (2019)
- [A data mining-based solution for detecting suspicious money laundering cases in an investment bank](https://mlsanity.com/paper/1609.00990/) (2016)

## Built on by (most cited first, 1 of 1)

- [A Privacy-Preserving Hybrid Federated Learning Framework for Financial Crime Detection](https://mlsanity.com/paper/2302.03654/) (2023)

## 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.