Fraud Detection in Supply Chain with Machine Learning

Mahdi Seify; Mehran Sepehri; Amin Hosseinian-far; Aryana Darvish

doi:10.1016/j.ifacol.2022.09.427

Fraud Detection in Supply Chain with Machine Learning

Mahdi Seify, Mehran Sepehri^*, Amin Hosseinian-far, Aryana Darvish

^*Corresponding author for this work

Research output: Contribution to Journal › Conference Article/Conference Proceedings › peer-review

Abstract

A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results.

Original language	English
Pages (from-to)	406-411
Number of pages	6
Journal	IFAC-PapersOnLine
Volume	55
Issue number	10
DOIs	https://doi.org/10.1016/j.ifacol.2022.09.427
Publication status	Published - 26 Oct 2022
Event	10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022 - La Cité des Congrès de Nantes 5, rue de Valmy BP 24102, 44041 Nantes Cedex 1, Nantes, France Duration: 22 Jun 2022 → 24 Jun 2022 https://hub.imt-atlantique.fr/mim2022/

Bibliographical note

Keywords

Machine Learning
Supply Chain
Fraud
Detection
pattern recognition

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10.1016/j.ifacol.2022.09.427Licence: CC BY-NC-ND

Cite this

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title = "Fraud Detection in Supply Chain with Machine Learning",

abstract = "A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results.",

keywords = "Machine Learning, Supply Chain, Fraud, Detection, pattern recognition",

author = "Mahdi Seify and Mehran Sepehri and Amin Hosseinian-far and Aryana Darvish",

note = "{\textcopyright} 2023 The Authors; 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022, MIM 2022 ; Conference date: 22-06-2022 Through 24-06-2022",

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TY - JOUR

T1 - Fraud Detection in Supply Chain with Machine Learning

AU - Seify, Mahdi

AU - Sepehri, Mehran

AU - Hosseinian-far, Amin

AU - Darvish, Aryana

PY - 2022/10/26

Y1 - 2022/10/26

N2 - A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results.

AB - A variety of fraud in Supply Chains may be detected either in physical parts or in cyber data. We use supervised machine learning to detect various fraud and misinformation in supply chains. The study is based on a car manufacturer concerned with increasing fraud, ranging from fraudulent invoices to inflated prices. Big data is provided for pattern recognition. A macro-level code is presented with actual algorithms developed in Python. The research is continuing, while the current work is presented with promising results.

KW - Machine Learning

KW - Supply Chain

KW - Fraud

KW - Detection

KW - pattern recognition

U2 - 10.1016/j.ifacol.2022.09.427

DO - 10.1016/j.ifacol.2022.09.427

M3 - Conference Article/Conference Proceedings

SN - 2405-8963

VL - 55

SP - 406

EP - 411

JO - IFAC-PapersOnLine

JF - IFAC-PapersOnLine

IS - 10

T2 - 10th IFAC Conference on Manufacturing Modelling, Management and Control MIM 2022

Y2 - 22 June 2022 through 24 June 2022

ER -

Fraud Detection in Supply Chain with Machine Learning

Abstract

Bibliographical note

Keywords

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