Corruption remains one of the most persistent and economically destructive challenges facing governments, corporations, and institutions worldwide. Despite extensive legislation, regulatory frameworks, and compliance programs, organizations continue to lose substantial financial resources through procurement fraud, unauthorized payments, conflicts of interest, collusion, and abuse of discretionary authority. A fundamental weakness of many anti-corruption efforts is their continued reliance on individual integrity and post-event enforcement rather than on systems deliberately designed to constrain misconduct before it occurs. This paper introduces the concept of Algorithmic Trust, a strategic management approach that embeds integrity directly into organizational architecture. Under this model, trust is not treated as a personal characteristic or cultural aspiration, but as a structural property engineered through internal controls, approval hierarchies, segregation of duties, audit trails, decision rules, and data-driven monitoring mechanisms. The central premise is straightforward yet transformative: organizations become more trustworthy when ethical behavior is systemically enforced and opportunities for corruption are structurally restricted. Drawing on research in anti-corruption, internal controls, corporate governance, procurement integrity, compliance automation, behavioral ethics, and information systems, this study argues that corruption should be understood primarily as a design failure rather than solely as a moral failure. When organizations concentrate authority, tolerate undocumented exceptions, and permit excessive managerial discretion, they create conditions that enable fraud and abuse. Conversely, when decision rights are distributed, transactions are automatically validated, and critical actions are digitally traceable, corruption becomes significantly more difficult to execute and conceal. To operationalize this concept, the paper develops the Algorithmic Trust Index (ATI), a diagnostic instrument that measures the extent to which integrity is embedded within institutional systems. The index evaluates five core dimensions: governance architecture, control automation, segregation of duties, auditability and transparency, and monitoring and exception management. The paper also introduces the RAAZ Algorithmic Trust Architecture™, a proprietary consulting framework that translates the research into a practical methodology for designing anti-corruption systems across public and private sector organizations. The strategic implication is profound. Sustainable integrity cannot be achieved by relying exclusively on the honesty of individuals or by reacting to misconduct after losses have occurred. It must be engineered into the operational DNA of the organization itself. Institutions that successfully embed algorithmic trust can reduce corruption exposure, strengthen accountability, enhance stakeholder confidence, and improve financial performance. Ultimately, this research advances a new paradigm in governance and management: the most resilient organizations are not those that merely hope for ethical behavior, but those that design systems in which corruption becomes structurally difficult, economically unattractive, and operationally visible. Through this perspective, Algorithmic Trust offers both a conceptual breakthrough and a practical blueprint for institutional integrity in the modern era.
AbedAlaziz AbuKhadrah (Sun,) studied this question.