The human-centric algorithm: synergising AI-Driven fraud detection with ethical human oversight to optimise sustainable banking integrity, Aminu Masanawa, Ahmad Shaheen

Aminu Usman Masanawa
DBA Research Scholar
Rushford Business School
Switzerland
ORCID: 0009-0005-4158-3075
Ahmad Shaheen
College of Management and Technology,
Arab Academy for Science, Technology and Maritime Transport
Egypt
ORCID: 0009-0002-5677-1990

Type of Paper: Research
Received: 19 December 2025 / Revised: 14 June 2026 / Accepted: 14 July 2026 / Published: 17 July 2026
DOI: 10.47556/J.WJEMSD.22.5.2026.4

Purpose: The research aims to analyse whether Artificial Intelligence (AI) and Machine Learning (ML) alone are efficient and self-sustainable in detecting financial fraud, or if they need human intervention and supervision for better fraud detection activity in digital finance.
Design/Methodology: The Kaggle credit card fraud dataset of 284,807 transactions was used to determine the grey zone of suspicious flagging. To analyse the occurrence of false positives, a linear regression model was applied to the first 10,00 anonymised banking transactions.
Findings: Results demonstrated a high degree of false-positive exposure in automated anomaly-detection systems. The regression analysis also yielded a statistically significant model (F = 4.255; p < 0.001), which underscores the complexity and unpredictability of fraud behaviour in online banking contexts.
Originality/Value: The current research advances the investigation into the likelihood of false positives detected by AI and whether further human supervision is required to intervene in the detection or validation of suspicious flagging.
Research Limitations: The study is limited to the first 1,000 observations from the dataset to ensure manageable statistical interpretation and analytical consistency.
Practical Implications: The research offers a transformational roadmap for applying human contextual judgement to banking ecosystems, balancing the predictive accuracy of AI with the moral responsibility of human intelligence to create a resilient, inclusive, and sustainable financial future.
Keywords: Digital Fraud Detection; Suspicious Transactions; Online Banking; Fraud Monitoring; Credit Card Fraud; Digital Finance.
Citation: Masanawa, A. U. and Shaheen, A. (2026): The human-centric algorithm: synergising AI-Driven fraud detection with ethical human oversight to optimise sustainable banking integrity. World Journal of Entrepreneurship, Management and Sustainable Development (WJEMSD), Vol. 22, No. 5, pp. xxx-xxx.