<abstract xmlns="http://www.w3.org/1999/xhtml">

<p>Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult. Financial institutions use rule-based systems to detect suspicious money laundering transactions. However, it suffers from large false positives (FPs) that lead to operational efforts or misses on true positives (TPs) that increase the compliance risk. This paper presents a study of convolutional neural network (CNN) to predict money laundering and employs SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) method to explain the CNN predictions. The results highlight the role of CNN in detecting suspicious transactions with high accuracy and SHAP’s role in bringing out the rationale of deep learning predictions.</p>
</abstract>

Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult. Financial institutions use rule-based systems to detect suspicious money laundering transactions. However, it suffers from large false positives (FPs) that lead to operational efforts or misses on true positives (TPs) that increase the compliance risk. This paper presents a study of convolutional neural network (CNN) to predict money laundering and employs SHapley Additive exPlanations (SHAP) explainable artificial intelligence (AI) method to explain the CNN predictions. The results highlight the role of CNN in detecting suspicious transactions with high accuracy and SHAP’s role in bringing out the rationale of deep learning predictions.

Explainable deep learning model for predicting money laundering transactions

Centre for Advanced Modelling and Geospatial Information Systems (CAMGIS), Faculty of Engineering and Information Technology

Department of Business Strategy and Innovation, Griffith Business School

Department of Geology and Geophysics, College of Science

International Journal on Smart Sensing and Intelligent Systems

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

{"article-title":"Explainable deep learning model for predicting money laundering transactions"}

Money laundering has been a global issue for decades. The ever-changing technology landscape, digital channels, and regulations make it increasingly difficult....

Explainable deep learning model for predicting money laundering transactions

Article Category: Article

Pubblicato online: 24 lug 2024

Pagine: -

Ricevuto: 10 apr 2024

DOI: https://doi.org/10.2478/ijssis-2024-0027

Parole chiave
deep learning, explainable AI, financial crime, money laundering, SHAP, suspicious transaction, CNN

© 2024 Dattatray Vishnu Kute et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Explainable deep learning model for predicting money laundering transactions

Article Category: Article

Pubblicato online: 24 lug 2024

Pagine: -

Ricevuto: 10 apr 2024

DOI: https://doi.org/10.2478/ijssis-2024-0027

Parole chiavedeep learning, explainable AI, financial crime, money laundering, SHAP, suspicious transaction, CNN

© 2024 Dattatray Vishnu Kute et al., published by Sciendo

This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.

Parole chiave
deep learning, explainable AI, financial crime, money laundering, SHAP, suspicious transaction, CNN