Fabian Sven Karst
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Karst
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Fabian Sven
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fabian.karst@unisg.ch

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  • Publication
    FinDEx: A Synthetic Data Sharing Platform for Financial Fraud Detection
    ( 2024-01-06)
    Fabian Sven Karst
    ;
    Mahei Li
    ;
    Jan Marco Leimeister
    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.
    Type: conference paper
    Journal: Hawaii International Conference on System Sciences (HICSS)
    URL: https://hdl.handle.net/10125/106897
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/119119
  • Publication
    Synthesizing Training Data with Generative Adversarial Networks: Towards the Design of a Data-Sharing Ecosystem Platform for Fraud Detection
    ( 2023-06-02)
    Fabian Sven Karst
    ;
    Mahei Li
    ;
    Leimeister, Jan Marco
    Financial fraud has a severe impact on the general population. While financial institutions have technological capabilities for fraud detection using intelligent AI systems, the delay until they have collected a sufficient size of fraudulent data to train their fraud detection models creates a costly vulnerability. One major challenge for quickly training data lies in the inability to share fraud detection training data with other financial institutions, due to data and privacy regulations. Thus, we create the concept for a data-sharing ecosystem platform that addresses data anonymity concerns by creating synthesized training data based on each institution’s fraud detection training data sets. We rely on the advantages of generative adversarial networks (GAN) to quickly construct a shared dataset for all ecosystem members. Applying design science research, this paper derives design knowledge based on financial fraud detection literature, data sharing between financial institutions, GANs and modular systems theory for the design of a plat-form architecture for data-sharing ecosystems.
    Type: conference paper
    Journal: International Conference on Design Science Research in Information Systems and Technology (DESRIST)
    URI: https://www.alexandria.unisg.ch/handle/20.500.14171/107616