DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection

Abhimanyu Bhowmik, Madhushree Sannigrahi, Deepraj Chowdhury, Ashutosh Dhar Dwivedi, Raghava Rao Mukkamala

Research output: Chapter in Book/Report/Conference proceedingArticle in proceedingsResearchpeer-review


The majority of financial transactions are now conducted virtually around the world. The widespread use of credit cards and online transactions encourages fraudulent activity. Thus, one of the most demanding real-world challenges is fraud detection. Unbalanced datasets, in which there are a disproportionately high number of non-fraud samples compared to incidents of fraud, are one of the key obstacles to effective fraud detection. A further factor complicating the learning process for cutting-edge machine learning classifiers is how quickly fraud behaviour changes. Thus, in this study, we suggest an efficient fraud detection methodology. We propose a unique nonlinear embedded clustering to resolve imbalances in the dataset, followed by a Deep Belief Network for detecting fraudulent transactions. The proposed model achieved an accuracy of 94% with a 70:30 ratio of training-validation dataset
Original languageEnglish
Title of host publicationProceedings - 2022 IEEE International Conference on Big Data, Big Data 2022
EditorsShusaku Tsumoto, Yukio Ohsawa, Lei Chen, Dirk Van den Poel, Xiaohua Hu, Yoichi Motomura, Takuya Takagi, Lingfei Wu, Ying Xie, Akihiro Abe, Vijay Raghavan
Number of pages10
Place of PublicationLos Alamitos, CA
Publication date2022
ISBN (Print)9781665480468
ISBN (Electronic)9781665480451
Publication statusPublished - 2022
EventIEEE International Conference on Big Data. IEEE BigData 2022 - Osaka International Convention Center (OICC), Osaka, Japan
Duration: 17 Dec 202220 Dec 2022
Conference number: 10


ConferenceIEEE International Conference on Big Data. IEEE BigData 2022
LocationOsaka International Convention Center (OICC)
Internet address


  • Financial fraud detection
  • Deep belief network
  • UMAP
  • Explainable AI

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