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

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

doi:10.1109/BigData55660.2022.10020494

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: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

6 Citations (Scopus)

Abstract

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 language	English
Title of host publication	2022 IEEE International Conference on Big Data (Big Data)
Editors	Shusaku 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 pages	10
Publisher	IEEE Communications Society
Publication date	20 Dec 2022
Pages	3033-3042
Article number	10020494
ISBN (Print)	978-1-6654-8046-8
ISBN (Electronic)	9781665480451
DOIs	https://doi.org/10.1109/BigData55660.2022.10020494
Publication status	Published - 20 Dec 2022
Externally published	Yes
Event	2022 IEEE International Conference on Big Data (Big Data) - Osaka, Japan Duration: 17 Dec 2022 → 20 Dec 2022

Conference

Conference	2022 IEEE International Conference on Big Data (Big Data)
Location	Osaka, Japan
Period	17/12/2022 → 20/12/2022

Keywords

Big Data
Data preprocessing
Deep learning
Finance
Measurement
Predictive models
Training
UMAP
DBSCAN
Deep Belief Network
Explainable AI
CTGAN
Financial Fraud Detection

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10.1109/BigData55660.2022.10020494

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Bhowmik, A., Sannigrahi, M., Chowdhury, D., Dwivedi, A. D., & Mukkamala, R. R. (2022). DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. In S. Tsumoto, Y. Ohsawa, L. Chen, D. Van den Poel, X. Hu, Y. Motomura, T. Takagi, L. Wu, Y. Xie, A. Abe, & V. Raghavan (Eds.), 2022 IEEE International Conference on Big Data (Big Data) (pp. 3033-3042). Article 10020494 IEEE Communications Society. https://doi.org/10.1109/BigData55660.2022.10020494

Bhowmik, Abhimanyu ; Sannigrahi, Madhushree ; Chowdhury, Deepraj et al. / DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. 2022 IEEE International Conference on Big Data (Big Data). editor / Shusaku Tsumoto ; Yukio Ohsawa ; Lei Chen ; Dirk Van den Poel ; Xiaohua Hu ; Yoichi Motomura ; Takuya Takagi ; Lingfei Wu ; Ying Xie ; Akihiro Abe ; Vijay Raghavan. IEEE Communications Society, 2022. pp. 3033-3042

@inproceedings{3dc82ea36450473e853be59e7d0a4d73,

title = "DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection",

abstract = "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.",

keywords = "Big Data, Data preprocessing, Deep learning, Finance, Measurement, Predictive models, Training, UMAP, DBSCAN, Deep Belief Network, Explainable AI, CTGAN, Financial Fraud Detection",

author = "Abhimanyu Bhowmik and Madhushree Sannigrahi and Deepraj Chowdhury and Dwivedi, {Ashutosh Dhar} and Mukkamala, {Raghava Rao}",

year = "2022",

month = dec,

day = "20",

doi = "10.1109/BigData55660.2022.10020494",

language = "English",

isbn = "978-1-6654-8046-8",

pages = "3033--3042",

editor = "Shusaku Tsumoto and Yukio Ohsawa and Lei Chen and {Van den Poel}, Dirk and Xiaohua Hu and Yoichi Motomura and Takuya Takagi and Lingfei Wu and Ying Xie and Akihiro Abe and Vijay Raghavan",

booktitle = "2022 IEEE International Conference on Big Data (Big Data)",

publisher = "IEEE Communications Society",

address = "United States",

note = "2022 IEEE International Conference on Big Data (Big Data) ; Conference date: 17-12-2022 Through 20-12-2022",

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Bhowmik, A, Sannigrahi, M, Chowdhury, D, Dwivedi, AD & Mukkamala, RR 2022, DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. in S Tsumoto, Y Ohsawa, L Chen, D Van den Poel, X Hu, Y Motomura, T Takagi, L Wu, Y Xie, A Abe & V Raghavan (eds), 2022 IEEE International Conference on Big Data (Big Data)., 10020494, IEEE Communications Society, pp. 3033-3042, 2022 IEEE International Conference on Big Data (Big Data), 17/12/2022. https://doi.org/10.1109/BigData55660.2022.10020494

DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. / Bhowmik, Abhimanyu; Sannigrahi, Madhushree; Chowdhury, Deepraj et al.
2022 IEEE International Conference on Big Data (Big Data). ed. / Shusaku Tsumoto; Yukio Ohsawa; Lei Chen; Dirk Van den Poel; Xiaohua Hu; Yoichi Motomura; Takuya Takagi; Lingfei Wu; Ying Xie; Akihiro Abe; Vijay Raghavan. IEEE Communications Society, 2022. p. 3033-3042 10020494.

Research output: Contribution to book/anthology/report/conference proceeding › Article in proceeding › Research › peer-review

TY - GEN

T1 - DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection

AU - Bhowmik, Abhimanyu

AU - Sannigrahi, Madhushree

AU - Chowdhury, Deepraj

AU - Dwivedi, Ashutosh Dhar

AU - Mukkamala, Raghava Rao

PY - 2022/12/20

Y1 - 2022/12/20

N2 - 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.

AB - 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.

KW - Big Data

KW - Data preprocessing

KW - Deep learning

KW - Finance

KW - Measurement

KW - Predictive models

KW - Training

KW - UMAP

KW - DBSCAN

KW - Deep Belief Network

KW - Explainable AI

KW - CTGAN

KW - Financial Fraud Detection

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U2 - 10.1109/BigData55660.2022.10020494

DO - 10.1109/BigData55660.2022.10020494

M3 - Article in proceeding

SN - 978-1-6654-8046-8

SP - 3033

EP - 3042

BT - 2022 IEEE International Conference on Big Data (Big Data)

A2 - Tsumoto, Shusaku

A2 - Ohsawa, Yukio

A2 - Chen, Lei

A2 - Van den Poel, Dirk

A2 - Hu, Xiaohua

A2 - Motomura, Yoichi

A2 - Takagi, Takuya

A2 - Wu, Lingfei

A2 - Xie, Ying

A2 - Abe, Akihiro

A2 - Raghavan, Vijay

PB - IEEE Communications Society

T2 - 2022 IEEE International Conference on Big Data (Big Data)

Y2 - 17 December 2022 through 20 December 2022

ER -

Bhowmik A, Sannigrahi M, Chowdhury D, Dwivedi AD, Mukkamala RR. DBNex: Deep Belief Network and Explainable AI based Financial Fraud Detection. In Tsumoto S, Ohsawa Y, Chen L, Van den Poel D, Hu X, Motomura Y, Takagi T, Wu L, Xie Y, Abe A, Raghavan V, editors, 2022 IEEE International Conference on Big Data (Big Data). IEEE Communications Society. 2022. p. 3033-3042. 10020494 doi: 10.1109/BigData55660.2022.10020494

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

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