The Effectiveness of User Input for Fraud Detection in the Supply Chain Management Field

This research paper investigates the effectiveness of user input to assist with fraud detection in the supply chain management industry. To research this, a software application is created to mimic a supply chain management system and an internal component is engineered to perform the fraud analysis of transactions recorded in a database. The analysis module makes use of the Logistic Regression and Decision Tree Machine Learning algorithms. The test results show that user input does assist with detecting fraudulent transactions if there is a large number of transactions present for analysis and training the system. The test results indicate that the better machine learning algorithm to use is Decision Trees. Based off the test results and training data, the use of user input does assist with detecting fraudulent activity in supply chain management if there is enough training and testing transaction data to analyze.

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    size: 1.21 MB | mime_type: application/pdf | date: 2023-01-03 | sha256: 57bcede

Metadata

Work Title The Effectiveness of User Input for Fraud Detection in the Supply Chain Management Field
Access
Open Access
Creators
  1. David R Tchintchin
Keyword
  1. User input
  2. Supply chain
  3. Fraud
  4. Fraud detection
  5. Logistic Regression
  6. Decision Trees
  7. Analysis
  8. Test and train split
License CC BY 4.0 (Attribution)
Work Type Masters Culminating Experience
Acknowledgments
  1. Parag Pendharkar
Publication Date November 18, 2022
DOI doi:10.26207/c4bn-s419
Deposited January 03, 2023

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Version 1
published

  • Created
  • Updated
  • Updated Acknowledgments Show Changes
    Acknowledgments
    • Parag Pendharkar
  • Added Creator Emily Mross
  • Added Creator David R Tchintchin
  • Added TCHINTCHIN_DAVID.pdf
  • Updated License Show Changes
    License
    • https://creativecommons.org/licenses/by/4.0/
  • Published

Version 2
published

  • Created
  • Deleted Creator Emily Mross
  • Updated Creator David R Tchintchin
  • Published
  • Updated Keyword, Publication Date Show Changes
    Keyword
    • User input, supply chain, fraud, fraud detection, Logistic Regression, Decision Trees, Analysis, test and train split
    • User input, Supply chain, Fraud, Fraud detection, Logistic Regression, Decision Trees, Analysis, Test and train split
    Publication Date
    • 2022
    • 2022-11-18