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

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 ques...

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Bibliographic Details
Published in:Journal of ambient intelligence and humanized computing 2022-10, Vol.13 (10), p.4663-4679
Main Authors: Chang, Jia-Wei, Yen, Neil, Hung, Jason C.
Format: Article
Language:English
Subjects:
Accuracy
Artificial Intelligence
Artificial neural networks
Chatbots
Classification
Computational Intelligence
Datasets
Design
Engineering
Finance
Fraud
Fraud prevention
Internet of Things
Machine learning
Mobile commerce
Natural language processing
Neural networks
Original Research
Robotics and Automation
Semantics
Smartphones
Social networks
Support vector machines
Target detection
User Interfaces and Human Computer Interaction
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Summary: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.
ISSN:1868-5137
1868-5145
DOI:10.1007/s12652-021-03512-2