Asian Research Journal of Current Science

The Paradox of Prevention: Why Measles and Rubella Persist in the Era of Effective Vaccines

Christopher Ononiwu Elemuwa — Fri, 01 May 2026 00:00:00 +0000

Measles and rubella remain major vaccine-preventable diseases that continue to cause significant morbidity and mortality, especially in Nigeria and other resource-limited settings of the Global South. This comprehensive review integrates molecular virology, epidemiology, immunology, and public health perspectives to assess the biology, transmission dynamics, and control strategies for these viruses. Measles, a highly contagious Morbillivirus with a basic reproduction number (R₀) of 12–18, and rubella, a teratogenic Rubivirus responsible for congenital rubella syndrome (CRS), both persist due to gaps in vaccination coverage, surveillance, and health system capacity. In Nigeria, suboptimal immunization—only about 54% first-dose measles vaccine coverage—combined with regional disparities and vaccine hesitancy, continues to fuel recurrent outbreaks. The introduction of the measles-rubella (MR) vaccine in 2023 represents a crucial milestone for CRS prevention. Molecular epidemiology shows that measles genotype B3 and rubella genotype 1 predominate in Africa, with molecular surveillance central to elimination verification.

The review highlights the economic benefits of vaccination, showing that measles-rubella immunization is highly cost-effective, averting millions of cases and disability-adjusted life years (DALYs) at minimal cost per DALY averted. Persistent immunosuppression following measles infection (“immune amnesia”) and rubella’s teratogenic impact underscore the urgency of sustained control efforts.

Policy recommendations emphasize strengthening routine and supplementary immunization, scaling up molecular surveillance, addressing vaccine hesitancy through community engagement, and bolstering health system resilience. Achieving measles and rubella elimination in Nigeria will require coordinated action across scientific, socioeconomic, and policy domains—linking advanced virological knowledge with equitable, community-driven public health delivery.

Beyond Detection: Reconciling Analytical Sensitivity with Biological Relevance in Toxicological Risk Assessment

Christopher Ononiwu Elemuwa — Thu, 14 May 2026 00:00:00 +0000

The modern era of analytical chemistry—propelled by advances in high-resolution mass spectrometry, tandem LC-MS/MS, and ambient ionization techniques—has ushered in an unprecedented capacity to detect synthetic chemical residues at picomolar and even femtomolar concentrations, often surpassing parts-per-trillion sensitivity. Yet this remarkable technological progress has precipitated a widening epistemic and regulatory chasm between what can be measured instrumentally and what constitutes biologically meaningful harm. This manuscript critically examines the systematic divergence between analytical detection capabilities and toxicological significance across three interconnected domains.

First, we interrogate the physiological mechanisms of endogenous detoxification, including Phase I functionalization (cytochrome P450-mediated oxidation, reduction, and hydrolysis), Phase II conjugation (glucuronidation, sulfation, glutathione conjugation, and N-acetylation), and Phase III transport processes (ATP-binding cassette efflux pumps and organic anion/cation transporters). We demonstrate that homeostatic resilience, adaptive stress responses, and hormetic dose–response relationships frequently render trace-level exposures physiologically inconsequential, even when analytically verifiable.

Second, we address the epistemological constraints of mixture toxicology, including the limitations of dose-addition and independent-action models, the challenges of identifying interaction thresholds for synergistic or antagonistic effects, and the statistical power constraints of high-dimensional mixture analyses. We critically evaluate the "cocktail effect" hypothesis, distinguishing documented synergistic interactions from speculative cumulative risk frameworks that lack empirical validation at environmentally relevant concentrations.

Third, we analyze the cognitive biases that distort public perception of chemical risk—including the affect heuristic, availability cascades, source confusion between natural and synthetic exposures, and the asymmetrical influence of precautionary framing on regulatory decision-making. We contextualize these biases within the broader sociology of scientific knowledge, examining how media amplification of single-study findings and the conflation of hazard identification with risk characterization drive disproportionate policy responses.

Building upon these analyses, we propose a three-tier risk-prioritization framework: Tier I comprises substances with robust epidemiological evidence of high-impact toxicity at environmentally relevant doses (e.g., certain organophosphates, legacy persistent organic pollutants, and confirmed endocrine-disrupting compounds); Tier II includes chemicals with moderate hazard profiles requiring targeted biomonitoring and exposure mitigation; and Tier III encompasses the vast majority of detectable synthetic contaminants whose trace-level presence, while analytically confirmable, lacks plausible mechanistic pathways to adverse health outcomes given endogenous detoxification capacity and realistic exposure scenarios. This framework is designed to redirect finite public health resources toward substances with demonstrated high-impact toxicity while contextualizing the actual risk posed by trace-level synthetic contaminants.

We further integrate insights from pharmacokinetic modeling (physiologically based pharmacokinetic [PBPK] approaches), toxicological epidemiology (including causal inference methods and exposure assessment validation), and risk psychology to argue for an evidence-based recalibration of regulatory thresholds and consumer priorities. We examine case studies including bisphenol A regulatory reversals, glyphosate hazard classification controversies, and per- and polyfluoroalkyl substances (PFAS) risk assessment evolution to illustrate how analytical detectability has been progressively decoupled from pathogenicity in both scientific discourse and policy formulation.

Finally, we consider the emerging dimension of the human microbiome as a metabolic interface between xenobiotic exposure and host physiology, evaluating whether microbiome-mediated biotransformation amplifies or attenuates toxicological risk. We conclude by emphasizing that detectability does not equate to pathogenicity, and that sustainable chemical safety governance requires explicit differentiation between analytical capability, hazard potential, and probabilistic risk—a distinction essential to preserving public trust in scientific institutions while optimizing the allocation of protective health resources.

Cancer Biology: Mechanisms, Hallmarks, and Therapeutic Insights

Sat, 16 May 2026 00:00:00 +0000

Cancer represents a complex and highly heterogeneous group of diseases characterized by uncontrolled cellular proliferation, genomic instability, and the capacity for local invasion and distant metastasis. This review provides an integrated and contemporary overview of cancer biology, highlighting the intricate genetic, molecular, and environmental mechanisms that drive malignant transformation. At its core, carcinogenesis results from the progressive accumulation of mutations in two principal classes of genes: oncogenes, whose activation promotes uncontrolled cell growth and survival, and tumor suppressor genes, whose inactivation removes essential regulatory constraints on cell division and genomic integrity. The widely accepted Hallmarks of cancer framework serves as a conceptual foundation for understanding these processes, encompassing capabilities such as sustained proliferative signaling, evasion of growth suppressors, resistance to apoptosis, replicative immortality, induction of angiogenesis, activation of invasion and metastasis, and evasion of immune surveillance. Beyond genetic alterations, the tumor microenvironment including stromal cells, immune cells, and extracellular matrix components plays an important role in shaping tumor behavior, progression, and response to therapy. Additionally, nonmutational mechanisms such as epigenetic reprogramming and phenotypic plasticity further contribute to tumor heterogeneity and therapeutic resistance. Recent advances in molecular biology and genomics have significantly transformed cancer diagnosis and treatment, leading to the emergence of precision oncology. This includes targeted therapies directed at specific molecular alterations and innovative immunotherapeutic approaches, such as immune checkpoint inhibitors, which enhance anti-tumor immune responses. By linking molecular insights to clinical applications, this review emphasizes how evolving knowledge in cancer biology continues to drive the development of more effective and personalized strategies for cancer management.

A Framework for Scalable API-First Development: Industry Trends and Enterprise Case Studies

Sourov Md Sadik Mahmud — Fri, 16 Jan 2026 00:00:00 +0000

This paper examines API-first development, a cornerstone of modern software engineering, analyzing adoption trends and challenges in building scalable, interoperable systems. Scalability, versioning, documentation, and interoperability issues, such as 35% of GraphQL integrations facing delays, pose significant hurdles. We propose a framework for scalable API design, integrating contract-first design, scalability mechanisms, interoperability standards, developer experience enhancements, and governance practices. Validated through a survey of 50 software engineers and enterprise case studies, the framework demonstrates improved performance and integration efficiency. The study draws on industry reports and case studies from leading enterprises, illustrating benefits like enhanced modularity and reduced integration times. Future research directions, including hybrid API models and AI-driven design, are outlined to advance API-first systems. This work provides actionable guidelines for software engineers to build robust API-driven architectures, contributing to the evolving API economy.

Financial Fraud and Forensic Accounting on Corporate Performance of Deposit Money Banks (DMBs) in Nigeria

Wed, 21 Jan 2026 00:00:00 +0000

This study examined the effect of financial fraud on the corporate performance of Deposit Money Banks (DMBs) in Nigeria, emphasizing the moderating role of forensic accounting and the causal relationships between fraud variables and bank performance. Panel data comprising 121 observations were analysed using descriptive statistics, Pearson correlation, fixed and random effects regression models, moderation analysis, and pairwise Granger causality tests. Corporate performance was measured by return on assets “(ROA), while financial fraud was captured through financial statement fraud, tax evasion fraud, and electronic fraud, including forensic accounting interaction terms. Results indicate that financial statement fraud positively and significantly affects reported performance in the absence of forensic accounting, suggesting earnings manipulation, while tax evasion fraud negatively influences performance. Electronic fraud showed no significant direct effect in the regression models; however, Granger causality tests revealed a unidirectional causal relationship from electronic fraud to corporate performance, indicating immediate financial consequences. Incorporating forensic accounting altered these relationships by weakening the artificially positive effect of financial statement fraud and intensifying the adverse impact of tax evasion fraud, thereby improving the model’s explanatory power. Diagnostic tests confirmed the absence of heteroskedasticity and specification errors, affirming model reliability. The study concludes that forensic accounting is crucial in mitigating the distortions caused by financial fraud, enhancing transparency, and sustaining performance in Nigerian DMBs. It recommends institutionalizing forensic accounting units, strengthening internal control systems, and enforcing regulatory oversight to reduce fraud risks and improve sector stability.

Effects of Seed Priming with Zn and N Solution on Improving Germination and Seedling Vigor of Rice

Win Htay Oo, Kyi Moe, Thu Zar, Kyaw Ngwe, Htay Htay Oo — Thu, 29 Jan 2026 00:00:00 +0000

The objective of this study was to investigate the effects of priming rice seeds with zinc (Zn) and nitrogen (N) solutions on germination and seedling vigor. The experiment was conducted from March to May 2024 at the Laboratory of the Department of Agronomy, Yezin Agricultural University, Myanmar, utilising a 4 × 3 factorial arrangement in a Completely Randomised Design (CRD) with four replications. In this experiment, factor A was four levels of nitrogen (N): N0 = 0% Urea (0 g N/100 mL), N1 = 0.10% Urea (0.046 g N/100 mL), N2 = 0.20% Urea (0.092 g N/100 mL), N3 = 0.30% Urea (0.138 g N/100 mL). Additionally, three levels of zinc (Zn) were applied: Zn0 = 0% ZnSO₄·7H₂O, Zn1 = 0.07% ZnSO₄·7H₂O, Zn2 = 0.14% ZnSO₄·7H₂O as factor B. The results indicated that the N2 treatment produced the highest germination percentage, vigor index, and shoot length, followed by N1 and N3 after 14 days of treatment. The N1 resulted in the greatest root length, root number, shoot dry weight, and root dry weight compared to N2 and N3. Conversely, N0 had the lowest performance in terms of seedling emergence and vigor. Among the Zn treatments, Zn1 achieved the highest germination percentage, vigor index, and shoot length compared to Zn2. However, Zn2 showed the highest root length, root number, shoot dry weight, and root dry weight among all the Zn treatments, while Zn0 resulted in the lowest seedling parameters across all treatments. The interaction of N and Zn treatments demonstrated enhanced germination, vigor, shoot length, root length, root number, shoot dry weight, and root dry weight. In the absence of priming with either Zn or N solutions, the germination and vigor of rice seedlings were significantly lower than those of others. The N0Zn0 treatment exhibited the poorest seedling parameters. In conclusion, priming rice seeds with N and Zn was effective in improving germination, seedling vigor, and overall seedling performance.

The Effect of Freshwater Quality Parameters on the Physiological Processes of Fish

Anuradha Singh, Kapil Kumar, Vijay Kumar Yadav, Jagvir Singh — Fri, 20 Mar 2026 00:00:00 +0000

Water is essential for life and sustainable livelihoods. This study explores water quality parameters and their effect on fish abundance and physiological in Govind Sagar Dam Lalitpur. Fish are considered as water quality indicators, especially due to their sensitivity to pollution. Changes in water quality significantly alter fish behavior, an important index of fish growth and health. Given the importance of this relationship to aquaculture practices, it is essential to understand how water quality dynamics affect fish behavior. Water quality parameters are critical determinants of fish performance, as they directly or indirectly influence feed utilisation. The health and productivity of fish are strongly affected by the physicochemical characteristics of the aquatic environment, including key parameters such as dissolved oxygen (DO), total dissolved solids (TDS), turbidity, ammonia, salinity, pH, and electrical conductivity (EC). In the present study, the recorded water quality parameters were within the following ranges: temperature 26.40–28.80 °C, pH 7.23–9.34, turbidity 5.3–10.6 NTU, electrical conductivity 134.17–216 µS cm⁻¹, and dissolved oxygen 6.1–6.9 mg L⁻¹. All measured physicochemical parameters remained within the acceptable limits for the survival and optimal growth of freshwater fish species. This study therefore seeks to examine the influence of water quality on fish production, with particular emphasis on the role of sustainable water management practices.

Psychological Influence of Substance Abuse on Academic Performance among Students of College of Education Kastina-Ala, Benue State, Nigeria

Ungwa Emmanuel Vandekan, Yange Bemgba, Stephanie Saa-Ter Tsav — Thu, 02 Apr 2026 00:00:00 +0000

This study examines the psychological influence of substance abuse on academic performance among students of College of Education Kastina-Ala, Benue State, Nigeria. The study assesses the influence of substance abuse on academic performance, with specific objectives to examine its influence and determine gender differences. Using a cross-sectional survey design, the study sampled 110 students (71 males, 39 females) from College of Education Kastina-Ala. Results revealed a significant negative relationship between substance abuse and academic performance (r = -0.67, p < 0.01). Male students had higher academic performance (mean = 3.31, SD = 0.342) than female students (mean = 3.12, SD = 0.382). Recommendations include awareness programs, counseling services, and strengthened policies with comprehensive support to mitigate substance abuse's negative impact.

Effect of Different Rates of Phosphorus and Sulphur Fertilization on the Growth, Yield, and Protein Content of Mung Bean (Vigna radiata L.)

Htay Htay Oo, Swe Swe Mar, Kyi Kyi Shwe, Phyu Thaw Tun — Fri, 03 Apr 2026 00:00:00 +0000

This study aimed to evaluate the effects of different rates of phosphorus (P) and sulphur (S) on the growth, yield, and protein content of mung bean (Vigna radiata L.), to determine the combined effects, and to identify the optimal combination for maximum productivity and protein content of mung bean. The field experiment was conducted at the Department of Soil and Water Science Farm, Yezin Agricultural University, Myanmar, during the monsoon season from May to August in 2025, and post-monsoon season from October 2025 to January 2026. The experiment was laid out in a 4 × 3 factorial arrangement in a randomized complete block design (RCBD) with three replications. Four P levels (0, 30, 60, and 90 kg P₂O₅ ha⁻¹) and three S levels (0, 20, and 40 kg S ha⁻¹) were tested. Growth parameters (plant height, crop growth rate (CGR), and branches plant^-1), yield components (pods plant^-1, seeds pod^-1, and 100-grain weight), grain yield, and protein content were recorded. The tallest plants (71.60 and 60.40 cm), maximum CGR (15.56 and 13.08 g m^-2 day^-1), branches plant^-1 (2.20 and 2.0), pods plant^-1(30.73 and 27.10), seeds pod^-1(12.47 and 11.23), highest yield (1632.20 and 1712.20 kg ha^-1 ) and protein content (24.33 and 25.37 %) were recorded at the application of 90 kg P₂O₅ ha⁻¹ + 40 kg S ha⁻¹ (P₃S₂) during both the monsoon and post-monsoon seasons. These parameters were not significantly different from those of 60 kg P₂O₅ ha⁻¹ + 20 kg S ha⁻¹ (P₂S₁). Therefore, P₂S₁ is suggested as the most suitable nutrient combination for maximizing mung bean productivity and protein content in the study area.

Effects of Plant Growth Regulators and Organic Additives on Shoot Regeneration of Myanmar Royal Orchid, Bulbophyllum auricomum Lindl., from in vitro Seedlings

Sat, 04 Apr 2026 00:00:00 +0000

Bulbophyllum auricomum is a royal orchid of Myanmar people due to its remarkable value and sweet fragrance. Because of notably limited in the growth and propagation, and also due to over exploitation and habitat destruction, it becomes now an endangered species. The aim of this study was to study the effect of plant growth regulators and organic additives on multiplication of B. auricomum. The research was conducted at tissue culture laboratory of Htone Bo Farm, DAR, Taunggyi, Shan State, Myanmar. In vitro germinated seedlings were used as the plant materials for shoot regeneration. The experimental design was two factors factorial arrangement in RCB design with factor A of different levels of 6-benzyl amino purine (BAP) and Kinetin (Kin) (1,3 mg L^-1) in combination with 0.50 mg L^-1NAA, and organic additives of potato, rice, banana each 100 g L^-1and factor B of Kayin, Dawei and Rakhine seedlings. Statistix version 8.0 and mean comparisons were performed using LSD at the 5% level. From this study, the organic additives fortified treatments shown the superior effects on shoot regeneration of selected B. auricomum cultivars. The medium each fortified with 100g L-¹ banana gave maximum number of shoots per explant and followed by 100 g L^-1 rice 15.33 shoots per explant and 13.00 on 100 g L^-1 potato. Twenty weeks after inoculation roots and bulbs were well developed. 100g L^-1 potato supplement medium produced the maximum number of roots (8.33) and (7.58) on 100 g L^-1 rice fortified medium, followed by that of banana accomplished medium (5.66). The incorporation of organic additives into culture media significantly enhanced in vitro shoot growth of B. auricomum, demonstrating its potential for commercial production. Extensive field trials are still required to assess the long-term survival and flowering consistency of the acclimatized plants.

A Machine Learning-Based Analysis of Prostate Cancer Cases in Delta State, Nigeria

Nwabenu Dominic Christian, Omonode Ejiro — Fri, 10 Apr 2026 00:00:00 +0000

Prostate cancer is the leading cause of cancer-related mortality among Nigerian males, with most diagnosed cases presenting at an advanced, incurable stage. The incidence rate of prostate cancer in Nigeria is 32.8 per 100,000 while the mortality rate is 16.3 per 100,000. Despite advancements in early-stage detection and screening programs available in Delta State, the prevalence of late-stage diagnosis and lack of knowledge subsists. This study applied four supervised machine learning classifiers — Logistic Regression, Decision Tree, Random Forest, and Support Vector Machine — to retrospective patient records from six healthcare institutions across Delta State, with the aim of identifying the clinical risk factors most strongly associated with advanced-stage diagnosis and building a predictive framework capable of distinguishing early- from late-stage disease. Secondary data was collected from 60 confirmed prostate cancer cases diagnosed between January 2015 and December 2023 from three tertiary referral centers and three general hospitals. The Models was trained on 80% of the pooled dataset and evaluated on the remaining 20% using accuracy, sensitivity, specificity, F1-score, and AUC-ROC. Results showed that 68.3% of cases were at Stage III or IV at the time of diagnosis. The mean age at presentation was 64.2 years, and three quarters of patients had PSA levels above 10 ng/m. The four strongest predictors of advanced-stage disease were PSA level (OR = 5.82), Gleason score 8–10 (OR = 4.37), age 65 years or above (OR = 3.14), and positive family history (OR = 2.41). Random Forest outperformed all three competing models, achieving 91.3% accuracy, 89.6% sensitivity, 92.8% specificity, and an AUC-ROC of 0.94. These findings show that supervised machine learning can effectively predict prostate cancer stage at diagnosis in Delta State using routinely collected clinical data, with Random Forest achieving the strongest classification performance. The results have direct implications for early detection policy and clinical triage in the region, and future research should prioritise prospective data collection, external model validation, and the development of deployable decision-support tools for primary healthcare settings.

Dividend Policy Metrics and Profitability of the Non-financial Firms Listed on the Nairobi Securities Exchange, Kenya

Samuel Mutugi Muchomba, Robert Kipkorir Cheruiyot — Thu, 16 Apr 2026 00:00:00 +0000

Dividend policy is a critical determinant of a firm’s financial performance because it influences investor attraction, resource allocation, and reinvestment decisions. Non-financial companies listed on the Nairobi Securities Exchange play a significant role in Kenya’s economy; however, their dividend payments have often been inconsistent due to volatile market conditions and economic constraints affecting profitability. This study examined the relationship between dividend policy indicators Dividend Payout Ratio (DPR), Dividend Yield (DY), Dividend Coverage Ratio (DCR), and Dividend Changes (DC) and the earnings capacity of non-financial firms listed on the exchange. The analysis was grounded in the Dividend Irrelevance Theory, Signalling Theory, and Agency Theory to explain how dividend practices influence firm profitability. A descriptive research design was adopted, covering a five-year period and utilizing financial and market data from selected non-financial firms. Data were obtained from audited annual reports and the exchange database, and were verified through multiple checks to ensure reliability and accuracy. Analytical results were presented using tables, graphs, and summary statistics. The findings revealed that all four dividend policy variables positively and significantly influenced firm profitability. Firms with balanced dividend payout ratios recorded higher returns on assets, suggesting that consistent dividend yields strengthen market confidence and enhance firm performance. Similarly, higher dividend coverage ratios signaled financial stability, while positive dividend changes improved investor perception and overall corporate performance. The study recommends that corporate managers adopt sustainable dividend strategies that balance shareholder returns with reinvestment needs. Regulators should strengthen guidelines to ensure transparent dividend practices, while investors should consider dividend indicators when making investment decisions. Overall, the study contributes to understanding dividend policy dynamics in emerging markets and provides practical insights for improving profitability and competitiveness among non-financial firms in Kenya.

Predicting Growth and Dry Matter Production of Tomato in Response to Exogenous Proline Application under Drought Stress

Zin Shwe Thar Nu, San Shwe Myint, Wai Wai Lwin, Pan Ei Ei Kyaw — Tue, 05 May 2026 00:00:00 +0000

Aims: To evaluate the drought response of tomato under varying levels of proline application, to build a growth model for leaf area and shoot dry weight under drought and proline sprays, and to evaluate the model performance.

Study Design: Split-plot design: main factor (water supply: well-watered, drought-stressed), sub-factor (proline: 0, 20, 40, 60, 80 ppm) with four replications.

Place and Duration of Study: Polyhouse at the Department of Horticulture, Yezin Agricultural University (YAU), between October 2024 and May 2025.

Methodology: There were two pot experiments (Expt.): Expt. 1 for parameterization, Expt. 2 for model evaluation. Withholding of water and proline sprays was employed at five-leaf stage. Drought response of relative leaf expansion rate (RLER) to fraction of transpirable soil water (FTSW) was fitted by a linear–plateau regression (LPR) at each level of proline sprays and related parameters were generated for model simulation. Daily leaf expansion rate under well-watered condition (LER_w) was calculated from plant leaf area (PLA) estimated from measurements of individual leaf length and width. In model, LER under drought (LER_d) was described as the product of LER_w and RLER and PLA was the integral of LER. For each water regime, shoot dry weight (SDW) was the product of specific shoot mass and PLA.

Results: Moderate proline levels (20-40 ppm) delayed the drought response of RLER, exhibited by lower FTSW thresholds (0.49-0.5) compared to control (0.75). Predicted PLA under well-watered condition showed higher goodness of fit than droughted condition (R²=0.60 vs 0.55). Simulated SDW represented more under drought than the well-watered condition (R²=0.57 vs 0.42). Model performance across proline levels revealed RMSD ranges of 112-286 for PLA and 0.85-2.26 for SDW, with accuracies of 0.88-0.96.

Conclusion: Current growth model showed varying drought responses to proline sprays with some magnitudes of errors, which needed further calibration and validation.

Artificial Intelligence and Its Role in Forensic Accounting Investigations

Mon, 18 May 2026 00:00:00 +0000

The integration of artificial intelligence (AI) into forensic accounting represents a significant transition from traditional manual auditing toward more efficient, accurate, and proactive fraud detection. This study examines the relationship between AI adoption and fraud detection effectiveness using survey data from 120 forensic accountants in Nigeria. Employing descriptive statistics, correlation, and multiple regression analyses, the study evaluates how AI techniques and organizational support influence forensic investigation outcomes. Findings reveal a strong positive relationship between AI usage and fraud detection effectiveness (β = 0.68, p < 0.001), with organizational support also contributing significantly (β = 0.22, p = 0.002). The model explains 70% of the variance in fraud detection effectiveness. Results support Fraud Triangle Theory (Cressey, 1953) and Agency Theory (Jensen & Meckling, 1976), demonstrating that AI reduces fraud opportunities and enhances monitoring mechanisms. The study highlights the need for ethical frameworks, regulatory oversight, and investment in AI capacity development in emerging economies.