NOLEGEIN- Journal of Leadership & Strategic Management

WHAT DO AUDIT DIRECTORS SAY? QUALITATIVE VALIDATION OF THE CAS CONTROL ADJUSTMENT → DETECTION PATHWAY

David Sunday Araoti — 2026-07-04

Purpose: Quantitative studies have established that Continuous Auditing Systems (CAS) are positively associated with Fraud Detection Effectiveness (FDE), and that Internal Control Integrity (ICI) mediates this relationship. However, the underlying mechanisms remain a “black box.” This study provides qualitative validation of the mediation pathway, exploring how real‑time monitoring translates into improved detection outcomes through control adjustments. Design/methodology/approach: Semi‑structured interviews were conducted with 15 audit directors (10 internal audit heads, 5 external audit partners) across banking, manufacturing, public sector, and professional services in Nigeria. Participants were purposively sampled from the Wave 2 respondents of a larger mixed‑methods study (Article 2). Transcripts were analysed using template analysis, with inter‑coder reliability (κ = 0.84). Data saturation was achieved after 12 interviews. Findings: Four themes emerged: (1) CAS alerts are interpreted primarily as indicators of control weaknesses rather than direct evidence of fraud; (2) alerts trigger formal (e.g., monthly control exception reviews) and informal (e.g., immediate access revocation) control adjustments; (3) adaptive controls reduce false positives and enhance genuine fraud detection; (4) without strong ICI, CAS alerts are ignored, rendering the system ineffective. These findings empirically validate the CAS → ICI → FDE mediation pathway. Practical implications: Organisations must reframe CAS as a diagnostic instrument for control improvement, not merely a detection tool. Establishing formal feedback loops (alerts → review → adjustment) is critical. CAS investments yield maximum returns only when the control environment is strong enough to act on alerts. Originality/value: This study provides rare qualitative evidence from an emerging economy that unpacks the “black box” of the mediation pathway. It moves beyond statistical association to explain the process of adaptive fraud risk intelligence, offering rich, contextual insights for both theory and practice.

The Intelligent Frontier: Orchestrating AI and Machine Learning for Advanced Financial Fraud Detection

Shipra Aggarwal — 2026-06-12

Traditional rule-based systems cannot match that scale. They are too slow, too rigid, and too reactive. This paper examines how AI and ML are reshaping fraud detection. It evaluates LSTMs, GNNs, and Transformers as detection tools. Mastercard reported a 20% uplift in fraud detection rates. The paper also maps two emerging frontiers: Agentic AI and Quantum Graph Neural Networks. Strategic guidance is offered throughout CFOs and finance leaders. Global financial crime reached $4.4 trillion in 2025. Fraud networks now deploy AI against the very systems designed to stop them. Static detection methods are no longer adequate. This paper provides a structured evaluation of AI and machine learning architectures for financial fraud detection. Supervised models, including Random Forest and Logistic Regression, form the operational baseline. Deep learning architecture extends this capability significantly. Long Short-Term Memory networks capture sequential transaction patterns. Graph Neural Networks expose hidden criminal networks across accounts. Transformers apply self-attention to behavioural context across full transaction histories. Generative Adversarial Networks address the persistent class imbalance problem in training data. The paper introduces the DLSG framework as a governance blueprint. It integrates deep learning performance with sector-specific compliance requirements. GDPR and CCPA obligations are embedded at the design level. Case studies from Mastercard and Visa demonstrate real-world impact. Mastercard achieved a 20% uplift in detection rates. Visa processes risk scores in under one second per transaction. Ethical risks, including algorithmic bias and explainability gaps, are examined. SHAP-based tools and federated learning are presented as mitigations. Two frontier technologies conclude the analysis: Agentic AI and Quantum Graph Neural Networks. Both will redefine fraud defence within the decade. Strategic recommendations guide CFOs and management accountants toward governance-first AI adoption.

A Study on the Reasons Behind NVIDIA’s Emergence as the Leading AI Company: Evidence from Primary Data

Mihirsinh Gohil — 2026-06-20

This study explores students’ perceptions regarding the major factors behind NVIDIA’s emergence as a global leader in the Artificial Intelligence (AI) industry. The research primarily focuses on understanding how technological innovation, leadership vision, and strategic planning contribute to the company’s competitive advantage and long-term success. A descriptive research design was adopted for the study. Primary data were collected through a structured questionnaire administered to IT and MBA students, while secondary data from journals, reports, and company sources were used to provide background information on NVIDIA’s growth and market position. The study analysed responses from 231 participants using descriptive statistics, Pearson correlation analysis, and multiple regression techniques. The findings reveal that respondents strongly perceive NVIDIA’s success as a result of continuous technological innovation, visionary leadership, and effective strategic planning. Among all variables, strategic planning emerged as the most significant predictor of competitive advantage, indicating that NVIDIA’s ability to align innovation with long-term business objectives has played a crucial role in its market leadership. The study further suggests that leadership vision and technological capabilities indirectly influence competitive success through strategic execution. Overall, the research highlights how future technology professionals interpret corporate success in the AI-driven business environment and emphasizes the growing importance of strategic management in technology-based industries.

The Role of Behavioral Accounting in Understanding Financial Decision-Making and Its Influence on Corporate Strategies

Mbonigaba Celestin — 2026-06-09

Behavioral accounting has emerged as a crucial field in understanding financial decision-making by integrating cognitive psychology with traditional accounting principles. This study examines how behavioral accounting influences corporate strategies, with a particular focus on mitigating cognitive biases, improving investment decisions, and enhancing financial transparency. The research employs a systematic literature review and statistical correlation analysis of secondary data from 2020 to 2024. Key findings reveal a strong correlation (r = 0.997) between behavioral accounting adoption and improvements in financial decision-making, with a regression analysis indicating that every 1% increase in behavioral accounting adoption leads to a 0.20% improvement in decision efficiency (β = 0.197, R2 = 0.986, p < 0.001). Companies that implemented behavioral insights experienced an 8% improvement in decision-making, a 3.5% increase in return on investment (ROI), and a 6% reduction in financial miscalculations. The study concludes that behavioral accounting significantly enhances corporate governance, risk assessment, and financial forecasting, reinforcing its importance in modern financial strategies. Recommendations include integrating behavioral analytics into corporate finance tools, increasing managerial awareness of cognitive biases, and aligning behavioral accounting practices with regulatory frameworks. These findings contribute to advancing behavioral finance theory and its practical applications in corporate decision-making.

Design Thinking and Innovation

Ganesh — 2026-07-04

In today's highly competitive and rapidly evolving business environment, organizations are continuously seeking innovative approaches to solve complex problems, enhance customer satisfaction, and maintain sustainable competitive advantages. One such approach that has gained significant attention in both academic and professional circles is Design Thinking (DT). Design thinking is a human-centered, creative, and systematic problem-solving methodology that integrates user needs, technological feasibility, and business viability to generate innovative solutions. The primary objective of this study is to examine the concept of design thinking and explore its role in fostering innovation within organizations. The study adopts a systematic literature review approach, analyzing relevant scholarly articles, books, and industry reports to provide a comprehensive understanding of the theoretical foundations, processes, and practical applications of design thinking. The review highlights the key stages of the design thinking process, including problem definition, research, ideation, prototyping, selection, implementation, and learning. Findings indicate that design thinking contributes significantly to organizational innovation by encouraging creativity, collaboration, user engagement, and continuous learning. Furthermore, it assists organizations in addressing complex and uncertain challenges while improving product development, service delivery, and strategic decision-making. The study also identifies major benefits, challenges, and opportunities associated with implementing design thinking in diverse organizational contexts. By synthesizing existing knowledge, this paper contributes to the growing body of literature on design thinking and provides valuable insights for managers, practitioners, and researchers seeking to leverage innovation as a source of competitive advantage and long-term organizational success.