Cost-Sensitive Deep Neural Networks for Financial Fraud Detection

Kruy SENG, Man Leung WONG

Research output: Other Conference ContributionsConference Paper (other)Other Conference Paperpeer-review

Abstract

Financial fraud has been a serious problem which attracted worldwide attention ranging from finance, economics, and management. Corporate fraud is one of the FBI's highest criminal priorities which caused destructive losses to investors, investor confidence, and country economy. It also contributed to a number of largest bankruptcies in history. In this paper, we propose a cost-sensitive learning approach based on Deep Neural Networks to solve financial fraud detection problem. We introduce cost information into the cost function of Deep Neural Networks to eliminate the bias toward non-fraudulent class. To demonstrate the effectiveness of the proposed method, we conduct comparison experiments with five machine learning and three ensemble learning approaches. One real-life data set of China Corporate Securities Fraud and one benchmark data from UCI repository are used in the experiments. The results show that our method is m
Original languageEnglish
Publication statusPublished - Aug 2017
EventThe 8th Annual Conference on ICT: Big Data, Cloud and Security - Singapore, Singapore, Singapore
Duration: 21 Aug 201721 Aug 2017
http://bigdataclouds.org/cwsd.php?Z3AuPTQ0Pg__/NDY_/TUZHIkZBRVZRZ2p8aWZ~ZH1waFx-fHliYHVoPH50bg__.pdf

Conference

ConferenceThe 8th Annual Conference on ICT: Big Data, Cloud and Security
Abbreviated titleICT-BDCS 2017
CountrySingapore
CitySingapore
Period21/08/1721/08/17
Internet address

Fingerprint

Finance
Cost functions
Learning systems
Costs
Experiments
History
Economics
Deep neural networks

Keywords

  • fraud
  • security
  • neural network
  • class imbalance

Cite this

SENG, K., & WONG, M. L. (2017). Cost-Sensitive Deep Neural Networks for Financial Fraud Detection. Paper presented at The 8th Annual Conference on ICT: Big Data, Cloud and Security, Singapore, Singapore.
SENG, Kruy ; WONG, Man Leung. / Cost-Sensitive Deep Neural Networks for Financial Fraud Detection. Paper presented at The 8th Annual Conference on ICT: Big Data, Cloud and Security, Singapore, Singapore.
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title = "Cost-Sensitive Deep Neural Networks for Financial Fraud Detection",
abstract = "Financial fraud has been a serious problem which attracted worldwide attention ranging from finance, economics, and management. Corporate fraud is one of the FBI's highest criminal priorities which caused destructive losses to investors, investor confidence, and country economy. It also contributed to a number of largest bankruptcies in history. In this paper, we propose a cost-sensitive learning approach based on Deep Neural Networks to solve financial fraud detection problem. We introduce cost information into the cost function of Deep Neural Networks to eliminate the bias toward non-fraudulent class. To demonstrate the effectiveness of the proposed method, we conduct comparison experiments with five machine learning and three ensemble learning approaches. One real-life data set of China Corporate Securities Fraud and one benchmark data from UCI repository are used in the experiments. The results show that our method is m",
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SENG, K & WONG, ML 2017, 'Cost-Sensitive Deep Neural Networks for Financial Fraud Detection' Paper presented at The 8th Annual Conference on ICT: Big Data, Cloud and Security, Singapore, Singapore, 21/08/17 - 21/08/17, .

Cost-Sensitive Deep Neural Networks for Financial Fraud Detection. / SENG, Kruy ; WONG, Man Leung.

2017. Paper presented at The 8th Annual Conference on ICT: Big Data, Cloud and Security, Singapore, Singapore.

Research output: Other Conference ContributionsConference Paper (other)Other Conference Paperpeer-review

TY - CONF

T1 - Cost-Sensitive Deep Neural Networks for Financial Fraud Detection

AU - SENG, Kruy

AU - WONG, Man Leung

PY - 2017/8

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N2 - Financial fraud has been a serious problem which attracted worldwide attention ranging from finance, economics, and management. Corporate fraud is one of the FBI's highest criminal priorities which caused destructive losses to investors, investor confidence, and country economy. It also contributed to a number of largest bankruptcies in history. In this paper, we propose a cost-sensitive learning approach based on Deep Neural Networks to solve financial fraud detection problem. We introduce cost information into the cost function of Deep Neural Networks to eliminate the bias toward non-fraudulent class. To demonstrate the effectiveness of the proposed method, we conduct comparison experiments with five machine learning and three ensemble learning approaches. One real-life data set of China Corporate Securities Fraud and one benchmark data from UCI repository are used in the experiments. The results show that our method is m

AB - Financial fraud has been a serious problem which attracted worldwide attention ranging from finance, economics, and management. Corporate fraud is one of the FBI's highest criminal priorities which caused destructive losses to investors, investor confidence, and country economy. It also contributed to a number of largest bankruptcies in history. In this paper, we propose a cost-sensitive learning approach based on Deep Neural Networks to solve financial fraud detection problem. We introduce cost information into the cost function of Deep Neural Networks to eliminate the bias toward non-fraudulent class. To demonstrate the effectiveness of the proposed method, we conduct comparison experiments with five machine learning and three ensemble learning approaches. One real-life data set of China Corporate Securities Fraud and one benchmark data from UCI repository are used in the experiments. The results show that our method is m

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SENG K, WONG ML. Cost-Sensitive Deep Neural Networks for Financial Fraud Detection. 2017. Paper presented at The 8th Annual Conference on ICT: Big Data, Cloud and Security, Singapore, Singapore.