Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)

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Stacking Model for Financial Fraud Detection with Synthetic Data

Authors
Zichuan Fu1, *
1Department of Computer Science, The University of Sheffield, Sheffield, S10 2TN, UK
*Corresponding author. Email: fzkuji@gmail.com
Corresponding Author
Zichuan Fu
Available Online 20 December 2022.
DOI
10.2991/978-94-6463-030-5_8How to use a DOI?
Keywords
Stacking Model; Financial Fraud Detection; Synthetic Financial Datasets; Logistic Regression; Support Vector Machines; Random Forest
Abstract

With the fast pace development of the Internet nowadays, financial frauds have also emerged continuously, which has seriously affected the development of the financial sector. Due to the lack of data in the financial field and the loose structure of transaction information, financial fraud detection remains a significant challenge. Based on the traditional machine learning model, this paper combines the three basic logistic regression models, support vector machine and random forest, and designs a two-layer stacking prediction model to detect financial transaction fraud. For unbalanced samples, this article uses up-sampling, under-sampling, and fusion methods to test and help search for optimal parameters through GridSearchcv. The final experiment shows that the Stacking model has a 97% recall rate and 87% accuracy for fraud samples on synthetic financial datasets. It can quickly detect most fraud samples while keeping false positives within a reasonable range. The model designed in this paper enriches the research of model fusion in financial fraud detection.

Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
Series
Atlantis Highlights in Intelligent Systems
Publication Date
20 December 2022
ISBN
10.2991/978-94-6463-030-5_8
ISSN
2589-4919
DOI
10.2991/978-94-6463-030-5_8How to use a DOI?
Copyright
© 2023 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Zichuan Fu
PY  - 2022
DA  - 2022/12/20
TI  - Stacking Model for Financial Fraud Detection with Synthetic Data
BT  - Proceedings of the 2022 International Conference on Bigdata Blockchain and Economy Management (ICBBEM 2022)
PB  - Atlantis Press
SP  - 60
EP  - 67
SN  - 2589-4919
UR  - https://doi.org/10.2991/978-94-6463-030-5_8
DO  - 10.2991/978-94-6463-030-5_8
ID  - Fu2022
ER  -