Anomaly detection in the shipping and banking industry

Ron Triepels

Research output: ThesisDoctoral ThesisScientific

34 Downloads (Pure)

Abstract

This thesis explores how organizations in the shipping and banking industry can apply anomaly detection to identify unwanted or risky events that threaten their operations. It consists of two parts. In the first part, we study how freight forwarders and customs authorities can apply anomaly detection to combat miscoding and smuggling in international shipping. Miscoding and smuggling are fraud schemes in which fraudsters provide falsified information about the goods in transit to evade shipping restrictions or customs duties. We develop a fraud detection system based on a Bayesian network to detect such fraud automatically in shipment data and compare the effectiveness of the system with traditional audit methods. In the second part, we study how central banks can apply anomaly detection to identify liquidity risk at banks from the transaction logs generated by financial market infrastructures. Liquidity risk arises when a bank manages its liquidity inadequately and is no longer able to meet its payment obligations. Identifying early warning signs of such risk is of importance to central banks to supervise the financial activities of banks. We develop various models to identify liquidity risk and evaluate their usefulness for bank supervision.
Original languageEnglish
QualificationDoctor of Philosophy
Awarding Institution
  • Tilburg University
Supervisors/Advisors
  • Daniels, Hennie, Promotor
  • Berndsen, Ron, Co-promotor
Award date13 Nov 2019
Place of PublicationTilburg
Publisher
Print ISBNs978 90 5668 609 3
Publication statusPublished - 2019

Fingerprint

Shipping industry
Liquidity risk
Anomaly detection
Banking industry
Shipping
Central bank
Smuggling
Fraud
Payment
Authority
Early warning
Financial markets
Usefulness
Audit
Obligation
Liquidity
Freight
Bayesian networks
Bank supervision
Fraud detection

Cite this

Triepels, R. (2019). Anomaly detection in the shipping and banking industry. Tilburg: CentER, Center for Economic Research.
Triepels, Ron. / Anomaly detection in the shipping and banking industry. Tilburg : CentER, Center for Economic Research, 2019. 139 p.
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Triepels, R 2019, 'Anomaly detection in the shipping and banking industry', Doctor of Philosophy, Tilburg University, Tilburg.

Anomaly detection in the shipping and banking industry. / Triepels, Ron.

Tilburg : CentER, Center for Economic Research, 2019. 139 p.

Research output: ThesisDoctoral ThesisScientific

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AB - This thesis explores how organizations in the shipping and banking industry can apply anomaly detection to identify unwanted or risky events that threaten their operations. It consists of two parts. In the first part, we study how freight forwarders and customs authorities can apply anomaly detection to combat miscoding and smuggling in international shipping. Miscoding and smuggling are fraud schemes in which fraudsters provide falsified information about the goods in transit to evade shipping restrictions or customs duties. We develop a fraud detection system based on a Bayesian network to detect such fraud automatically in shipment data and compare the effectiveness of the system with traditional audit methods. In the second part, we study how central banks can apply anomaly detection to identify liquidity risk at banks from the transaction logs generated by financial market infrastructures. Liquidity risk arises when a bank manages its liquidity inadequately and is no longer able to meet its payment obligations. Identifying early warning signs of such risk is of importance to central banks to supervise the financial activities of banks. We develop various models to identify liquidity risk and evaluate their usefulness for bank supervision.

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Triepels R. Anomaly detection in the shipping and banking industry. Tilburg: CentER, Center for Economic Research, 2019. 139 p. (CentER Dissertation Series).