Fraud in higher education: a system for detection and prevention

Abstract

Purpose Academic corruption and fraudulent practices have become problematic in recent years. Governments around the world have introduced dedicated higher education commissions to regulate higher education providers. The purpose of this paper is to design a system for the detection and prevention framework of fraudulent behaviour in higher education. Design/methodology/approach This paper performs a survey on academic misconduct practices and expands the survey by analysing the accreditation process. This study further identifies common corrupt practices in the accreditation process with reference to particular accreditation standards or laws. If the accreditation process is as thorough as, this paper is led to believe, a higher institute may stop being compliant immediately after the accreditation process. playing a catch-me-if-you-can at the next accreditation cycle. The survey of the accreditation process and identification of corrupt practices lead to an identification of preventative and detective measures. Findings The review of accreditation procedures and conditions identifies that fraudulent practices can occur at every part of any policy and procedure. The framework prevents repudiation and allows for spontaneous investigations internally and externally. The blockchain prevented changes to the system and allow for auditing of changes. A system such as this could suppress accreditation fraud and minimise its corrupt impact. Not to mention identify with relative ease the severity and life of corrupt practice. Originality/value Contributions are made in the framework for detecting and preventing corrupt practices in Higher Education using blockchain immutable transactions. This enables real-time accreditation compliance checks and monitoring of conditions. External complaints or reviews can be conducted with minimum interactions from higher education providers.

Cite this work

No public copy is stored for this record, so only the metadata is cited.

Stored only when both papers are in the corpus — 19 citations exist upstream.

Papers that cite the same references — the relational fallback while embeddings are still backfilling.