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Design of a NLP-empowered finance fraud awareness model: the anti-fraud chatbot for fraud detection and fraud classification as an instance

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Abstract

Advanced technologies, Internet of things and fundamental information communication technology frameworks in particular, facilitate information sharing. One simple click-on end device can make every tool accessible to users; however, whether correct information is received remains to be an open question. Incorrect information that bundles the factors of fake, malicious, or fraudulent information, whether deliberately or not, may worsen misunderstandings. To avoid these cases escalating to the level of crime, a universal financial fraud-awareness model was designed in this study. The model first targets accurate fraud detection and classification using the natural language processing technique. An anti-fraud chatbot is then implemented as an instance of the model and deployed on a widely used social network service, namely LINE. This implementation aims to manage finance-fraud cases and provide anti-fraud suggestions to deal with foreseeable fraud events. Statistics of the comparison between Word2vec, ELMO, BERT, and DistilBERT on the five-strong conventional machine-learning models and the models of artificial neural networks indicate that the proposed model can achieve an accuracy of over 98% while detecting potential finance-fraud cases. In addition, the more efficient models by DistilBERT with a support vector machine or a random forest have lower resource-computation cost and faster execution time in real applications.

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Availability of data and material

Data, experiment dataset as well, is accessible per reasonable request.

Code Availability

Code will be available per reasonable request after the patent is filed.

Notes

  1. https://cofacts.g0v.tw/.

  2. https://www.mygopen.com/.

  3. https://165.npa.gov.tw/.

  4. https://www.checkcheck.me/.

  5. https://cofacts.g0v.tw/.

  6. https://165.npa.gov.tw/.

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Acknowledgements

Special thanks to Mr. Hou-Hsun Wang for his assistance in the development of the programming for this study.

Funding

This work was partially supported by the Ministry of Science and Technology, Taiwan, R.O.C. [grand number MOST 108-2218-E-025-002-MY3].

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Authors and Affiliations

  1. Department of Computer Science and Information Engineering, National Taichung University of Science and Technology, Taichung City, Taiwan

    Jia-Wei Chang & Jason C. Hung

  2. School of Computer Science and Information Security, Guilin University of Electronic Technology, Guilin, China

    Neil Yen

  3. School of Computer Science and Engineering, University of Aizu, Aizuwakamatsu, Japan

    Neil Yen

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  1. Jia-Wei Chang
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  2. Neil Yen
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Correspondence to Neil Yen.

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Appendix

Appendix

See Table 15.

Table 15 Sample Fraud Events and Categories
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Chang, JW., Yen, N. & Hung, J.C. Design of a NLP-empowered finance fraud awareness model: the anti-fraud chatbot for fraud detection and fraud classification as an instance. J Ambient Intell Human Comput 13, 4663–4679 (2022). https://doi.org/10.1007/s12652-021-03512-2

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  • DOI: https://doi.org/10.1007/s12652-021-03512-2

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