FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection
F. Karst, M. Li, and J. Leimeister. Hawaii International Conference on System Sciences (HICSS), Waikiki, Hawaii, USA, University of Hawaiʻi at Mānoa, (2024)
Abstract
The rising number of financial frauds inflicted in the last year more than 800 billion USD in damages on the global economy. Although financial institutions possess advanced AI systems for fraud detection, the time required to accumulate a sufficient volume of fraudulent data for training models creates a costly vulnerability. Combined with the inability to share fraud detection training data among institutions due to data and privacy regulations, this poses a major challenge. To address this issue, we propose the concept of a synthetic data-sharing ecosystem platform (FinDEx). This platform ensures data anonymity by generating synthesized training data based on each institution's fraud detection datasets. Various synthetic data generation techniques are employed to rapidly construct a shared dataset for all ecosystem members. Using design science research, this paper leverages insights from financial fraud detection literature, data sharing practices, and modular systems theory to derive design knowledge for the platform architecture. Furthermore, the feasibility of using different data generation algorithms such as generative adversarial networks, variational auto encoder and Gaussian mixture model was evaluated and different methods for the integration of synthetic data into the training procedure were tested. Thus, contributing to the theory at the intersection between fraud detection and data sharing and providing practitioners with guidelines on how to design such systems.
%0 Conference Paper
%1 ls_leimeister
%A Karst, Fabian
%A Li, Mahei Manhai
%A Leimeister, Jan Marco
%B Hawaii International Conference on System Sciences (HICSS)
%C Waikiki, Hawaii, USA
%D 2024
%K Data_Ecosystem Data_Scarcity Financial_Services Fraud_Detection Hybrid_Intelligence Sharing_Platform Synthetic_Data itegpub pub_jml pub_mli
%T FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection
%U http://pubs.wi-kassel.de/wp-content/uploads/2024/01/JML_957.pdf
%X The rising number of financial frauds inflicted in the last year more than 800 billion USD in damages on the global economy. Although financial institutions possess advanced AI systems for fraud detection, the time required to accumulate a sufficient volume of fraudulent data for training models creates a costly vulnerability. Combined with the inability to share fraud detection training data among institutions due to data and privacy regulations, this poses a major challenge. To address this issue, we propose the concept of a synthetic data-sharing ecosystem platform (FinDEx). This platform ensures data anonymity by generating synthesized training data based on each institution's fraud detection datasets. Various synthetic data generation techniques are employed to rapidly construct a shared dataset for all ecosystem members. Using design science research, this paper leverages insights from financial fraud detection literature, data sharing practices, and modular systems theory to derive design knowledge for the platform architecture. Furthermore, the feasibility of using different data generation algorithms such as generative adversarial networks, variational auto encoder and Gaussian mixture model was evaluated and different methods for the integration of synthetic data into the training procedure were tested. Thus, contributing to the theory at the intersection between fraud detection and data sharing and providing practitioners with guidelines on how to design such systems.
%@ 978-0-9981331-7-1
@inproceedings{ls_leimeister,
abstract = {The rising number of financial frauds inflicted in the last year more than 800 billion USD in damages on the global economy. Although financial institutions possess advanced AI systems for fraud detection, the time required to accumulate a sufficient volume of fraudulent data for training models creates a costly vulnerability. Combined with the inability to share fraud detection training data among institutions due to data and privacy regulations, this poses a major challenge. To address this issue, we propose the concept of a synthetic data-sharing ecosystem platform (FinDEx). This platform ensures data anonymity by generating synthesized training data based on each institution's fraud detection datasets. Various synthetic data generation techniques are employed to rapidly construct a shared dataset for all ecosystem members. Using design science research, this paper leverages insights from financial fraud detection literature, data sharing practices, and modular systems theory to derive design knowledge for the platform architecture. Furthermore, the feasibility of using different data generation algorithms such as generative adversarial networks, variational auto encoder and Gaussian mixture model was evaluated and different methods for the integration of synthetic data into the training procedure were tested. Thus, contributing to the theory at the intersection between fraud detection and data sharing and providing practitioners with guidelines on how to design such systems.},
added-at = {2024-01-03T12:33:19.000+0100},
address = {Waikiki, Hawaii, USA},
author = {Karst, Fabian and Li, Mahei Manhai and Leimeister, Jan Marco},
biburl = {https://www.bibsonomy.org/bibtex/2ea19fbaaf6fb064d3a7f32bb49777184/ls_leimeister},
booktitle = {Hawaii International Conference on System Sciences (HICSS)},
eventdate = {03-06 Jan 2023},
eventtitle = {Hawaii International Conference on System Sciences (HICSS)},
interhash = {23aa6fe4cff70e1f63fcdc3ed95e6ac3},
intrahash = {ea19fbaaf6fb064d3a7f32bb49777184},
isbn = {978-0-9981331-7-1},
issn = {2572-6862},
keywords = {Data_Ecosystem Data_Scarcity Financial_Services Fraud_Detection Hybrid_Intelligence Sharing_Platform Synthetic_Data itegpub pub_jml pub_mli},
language = {English},
organization = {University of Hawaiʻi at Mānoa},
timestamp = {2024-01-14T17:37:02.000+0100},
title = {FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection},
url = {http://pubs.wi-kassel.de/wp-content/uploads/2024/01/JML_957.pdf},
venue = {Waikiki, Hawaii, USA},
year = 2024
}