Triangular Perspectives on Big Data-Enhanced Healthcare Fraud Detection in NHS England: Integrating Opportunity, Rationalisation, and Pressure

Abstract

The National Health Service (NHS) in England is a cornerstone of public healthcare, providing essential services to millions of people. However, healthcare fraud poses a serious challenge to the sustainability and integrity of the NHS. Fraudulent activities not only lead to significant financial losses but also divert resources from patient care, compromising the quality of services and eroding public trust. This thesis addresses the issue of healthcare fraud within NHS England by applying the fraud triangle theory, which examines three key factors that contribute to fraudulent behaviour: opportunity, rationalisation, and pressure/incentive.

Chapters 3–5 of this thesis each examine potential fraud risk in the NHS through the Fraud Triangle framework, with each chapter primarily focusing on one element while still considering all three. Chapter 3 focuses on the opportunity for fraud by analysing hospital spending and performance indicators. It highlights how weak financial controls and performance metrics can create opportunities for fraud, particularly through the manipulation of data and expenses. Chapter 4 addresses the rationalisation of fraud, examining how internal processes such as quality assurance, data reconciliation, and benchmarking can either deter or enable fraudulent activities. A key finding of this chapter is the misuse of these mechanisms to justify unethical behaviour. Chapter 5 explores the pressure or incentives to commit fraud by analysing UK National Audit Office reports using advanced text mining techniques. This analysis reveals how financial pressures, particularly those arising from large contracts and crises such as the COVID-19 pandemic, can drive fraudulent behaviour within the healthcare sector.

The thesis makes significant theoretical contributions by advancing the Fraud Triangle in the context of public healthcare fraud and situating it within the broader framework of agency theory. It provides a deeper understanding of how organisational and systemic factors within NHS England create opportunities for fraud, how rationalisation is embedded within healthcare practices, and how external pressures, such as financial constraints, incentivise fraudulent activities. Furthermore, the use of big data analytics and artificial intelligence offers a novel approach to detecting healthcare fraud and identifying patterns across large datasets.

In terms of practical contributions, this thesis provides valuable insights for policymakers, healthcare administrators, and oversight bodies. The findings highlight the need for stronger financial controls, improved quality assurance mechanisms, and enhanced governance structures to prevent fraud. Additionally, it calls for the revision and improvement of benchmarking practices and auditing systems to ensure they do not become tools for rationalising fraudulent behaviour. This research also supports the development of more effective fraud detection strategies, offering a foundation for future policy development and the implementation of better fraud prevention measures within the NHS and other public healthcare systems.