A new framework of feature engineering for machine learning in financial fraud detection

Ikeda, Chie, Ouazzane, Karim and Yu, Qicheng (2020) A new framework of feature engineering for machine learning in financial fraud detection. In: 10th International Conference on Artificial Intelligence, Soft Computing and Applications (AIAA 2020), 28-29 November 2020, London,UK.

The final version_feature engineering framework for financial fraud detection model.pdf - Accepted Version
Available under License Creative Commons Attribution Non-commercial No Derivatives 4.0.

Download (663kB) | Preview
Official URL: https://www.academia.edu/44672666/A_NEW_FRAMEWORK_...

Abstract / Description

Financial fraud activities have soared despite the advancement of fraud detection models empowered by machine learning (ML). To address this issue, we propose a new framework of feature engineering for ML models. The framework consists of feature creation that combines feature aggregation and feature transformation, and feature selection that accommodates a variety of ML algorithms. To illustrate the effectiveness of the framework, we conduct an experiment using an actual financial transaction dataset and show that the framework significantly improves the performance of ML fraud detection models. Specifically, all the ML models complemented by a feature set generated from our framework surpass the same models without such a feature set by nearly 40% on the F1-measure and 20% on the Area Under the Curve (AUC) value.

Item Type: Conference or Workshop Item (Paper)
Uncontrolled Keywords: financial fraud detection; feature engineering; feature creation; feature selection; machine learning
Subjects: 000 Computer science, information & general works
Department: School of Computing and Digital Media
Depositing User: Bal Virdee
Date Deposited: 16 Mar 2021 09:32
Last Modified: 16 Mar 2021 09:32
URI: https://repository.londonmet.ac.uk/id/eprint/6407


Downloads per month over past year

Downloads each year

Actions (login required)

View Item View Item