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Research Article
AI Adoption and Recruitment Efficiency in European Banking: A Mixed-Method Analysis
Dawid Krystian Prestini*
Issue:
Volume 1, Issue 1, March 2026
Pages:
1-6
Received:
20 August 2025
Accepted:
3 February 2026
Published:
21 February 2026
Abstract: The adoption of Artificial Intelligence (AI) is reshaping recruitment processes in the European banking sector, where efficiency, accuracy, and compliance are strategic imperatives. This study investigates the extent to which AI improves recruitment efficiency, candidate selection quality, organisational outcomes, and candidate trust. Using a mixed-method approach, data were collected from 200 HR professionals and managers in European banks and supplemented with secondary industry evidence. Descriptive statistics, correlation, and regression analyses confirm that AI-driven recruitment significantly reduces time-to-hire and improves candidate-job matching, with recruitment process efficiency (β = 0.562, p < 0.001) and structured evaluation criteria (β = 0.377, p = 0.002) emerging as the strongest predictors of positive organisational outcomes. However, results also indicate that excessive reliance on automation can negatively affect candidate trust (β = −0.259, p < 0.05). These findings extend theoretical debates by applying the Technology Acceptance Model, the Resource-Based View, and Human Capital Theory to the context of banking recruitment, highlighting AI as both a strategic resource and a source of ethical and transparency challenges. Practical implications include the need for hybrid recruitment models combining automation with human oversight, enhanced transparency in candidate communication, and strict alignment with the EU AI Act. This study contributes original empirical evidence from European banking, offering theoretical, managerial, and policy insights into the responsible and effective adoption of AI in recruitment.
Abstract: The adoption of Artificial Intelligence (AI) is reshaping recruitment processes in the European banking sector, where efficiency, accuracy, and compliance are strategic imperatives. This study investigates the extent to which AI improves recruitment efficiency, candidate selection quality, organisational outcomes, and candidate trust. Using a mixed...
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Review Article
Advancing IT Competency for Digital Transformation and Learning Ecosystem Expansion: An Integrative Review and Practical Framework
Mohammed Jahed Sarwar*
Issue:
Volume 1, Issue 1, March 2026
Pages:
7-13
Received:
15 December 2025
Accepted:
24 December 2025
Published:
21 February 2026
Abstract: Digital transformation has shifted from a technology modernization agenda to a capability agenda—one that depends on how effectively organizations develop, govern, and sustain IT competencies across cloud platforms, cybersecurity, data and AI, and digitally mediated learning and service ecosystems. This integrative review synthesizes peer-reviewed research and authoritative standards (2018-2025) to identify core competency domains and organizational enablers required to transcend incremental, legacy-bound models. Findings converge on five domains—(1) cloud and platform engineering, (2) cybersecurity and digital trust, (3) data, AI and automation (including generative AI governance), (4) experience engineering and service design, and (5) learning ecosystem engineering—supported by cross-cutting mechanisms in architecture, risk management, talent pathways, and change leadership. Building on this synthesis, the paper proposes an actionable competency-based framework and a phased implementation roadmap that organizations can use to assess readiness, prioritize investments, and operationalize continuous upskilling. The central contribution is practical and policy-relevant: it positions IT competency as the enterprise control point that converts technology investments into resilience, inclusion, and measurable operational performance in the information age.
Abstract: Digital transformation has shifted from a technology modernization agenda to a capability agenda—one that depends on how effectively organizations develop, govern, and sustain IT competencies across cloud platforms, cybersecurity, data and AI, and digitally mediated learning and service ecosystems. This integrative review synthesizes peer-reviewed ...
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Research Article
Performance Evaluation of Hybrid Bert Model on
Code-mixed for Hausa-English Using Adapted Pre-trained Data
Ali Garba Jakwa*,
Faseki Ngozi Franscisca,
Abubakar Yunusa Ahmad,
Musa Ibrahim
Issue:
Volume 1, Issue 1, March 2026
Pages:
14-26
Received:
8 October 2025
Accepted:
27 January 2026
Published:
21 February 2026
Abstract: This research evaluates the potentials of using BERT (Bidirectional Encoder Representations from Transformers) language model on code-mixed for English-Hausa Language code-mixed using adapted pre-trained dataset. The main aim of this research was to unveil the potential benefits of using pre-trained models for handling code-mixed data to improved language understanding and context sensitivity in relation to Hausa-English-Language, the objective of this research was achieved by developing a BERT model that is capable of handling Hausa-English code-mixed dataset exploring different machine learning language models by training the chosen model with the adapted English-Hausa Language code-mixed. What necessitates this research was due to low data corpus on the language domain of Hausa-English code-mixed while other languages were explored like English-Hindu Code-Mixed. The model was developed using python transformer library. The adapted pre-trained dataset was first pre-processed, tokenized and fine-tuned in order to fit into the BERT model, the dataset was normalized in the context of code-mixed conversation based on annotate language labels to distinguish between English and Hausa Language segments in the code-mixed text, appropriate parameter for training were set with different optimization strategies for fine-tuning, adjusted learning rate, batch sizes and training epochs for performance optimization. The model was evaluated based on accuracy, F1-score, precision and recall for Code-Mixed tasks, the results of HauBERT our proposed model showed more than 90% accuracy, the result was compared with state-of-the-art BERT language models, and the study recommended that this adapted pre-trained model should be applied in large language model for language understanding and context sensitivity.
Abstract: This research evaluates the potentials of using BERT (Bidirectional Encoder Representations from Transformers) language model on code-mixed for English-Hausa Language code-mixed using adapted pre-trained dataset. The main aim of this research was to unveil the potential benefits of using pre-trained models for handling code-mixed data to improved l...
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Research Article
AI and Cloud Computing in the Energy Industry: Impact on Data Security, Scalability, and Integration Challenges
Boye Aziboledia Frederick*
Issue:
Volume 1, Issue 1, March 2026
Pages:
27-40
Received:
17 September 2025
Accepted:
21 January 2026
Published:
24 February 2026
Abstract: The objective of this study is to analyze and evaluate the role of artificial intelligence (AI) and cloud computing in transforming the energy industry, with a focus on their impact on data security, scalability, and system integration. The rapid integration of these technologies is reshaping the energy industry by driving digital transformation, optimizing operations, and enabling data-driven decision-making. The paper is driven with mixed-research methods categorizing reviewed materials of fifty-six (56) into five (5) distinct categories, empirical/experimental, review/literature-based, theorical/conceptual, industry/technical report and online articles/experts’ commentaries. In the study a total number of thirty-two (32) among the reviewed literatures on impact on data security has 12, scalability span ten (10) literatures, whereas integration challenges take eleven (11) materials in the study. The article highlights the challenges of safeguarding sensitive energy data in distributed environments, managing scalability demands in response to increasing data volumes, and addressing interoperability issues between legacy systems and modern cloud-based architectures. Through a comprehensive analysis, the study underscores the critical need for robust security frameworks, scalable cloud strategies, and seamless integration models to ensure resilience and sustainability in the energy sector. The findings emphasize that while AI and cloud computing present transformative opportunities, their successful adoption depends on effectively mitigating risks and aligning technological innovation with industry-specific regulatory and operational requirements.
Abstract: The objective of this study is to analyze and evaluate the role of artificial intelligence (AI) and cloud computing in transforming the energy industry, with a focus on their impact on data security, scalability, and system integration. The rapid integration of these technologies is reshaping the energy industry by driving digital transformation, o...
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Research Article
Applications, Benefits, and Ethical Challenges of Artificial Intelligence in Palliative Care
Velibor Bozic*
Issue:
Volume 1, Issue 1, March 2026
Pages:
41-48
Received:
10 July 2025
Accepted:
26 February 2026
Published:
9 March 2026
Abstract: Artificial intelligence (AI) is increasingly recognized as a transformative force in healthcare, with growing relevance in palliative care. This article examines the clinical potential, current applications, risks, and ethical preconditions associated with AI implementation in this sensitive field. AI-driven systems enhance personalized symptom management by analyzing large datasets derived from electronic health records (EHRs), patient-reported outcomes, and clinical assessments. Machine learning algorithms identify patterns in symptom trajectories and treatment responses, enabling individualized care plans. Reported median prognostic accuracies range between 78% and 83% for survival prediction in advanced illness populations, while prediction of treatment response and pain management outcomes achieves approximately 80–85% accuracy. AI applications also contribute to caregiver support through chatbots and digital platforms providing continuous informational and emotional assistance, and to system-level improvements via symptom-tracking applications, virtual reality tools, and AI-supported care coordination systems. Furthermore, AI strengthens research capacity by enabling large-scale data analysis and identifying novel risk factors, such as delirium prediction models with sensitivity up to 75% and specificity up to 88%. Despite these advantages, implementation raises ethical and practical concerns, including data privacy risks, algorithmic bias, model inaccuracy, high costs, and limited trust among patients and caregivers. Safe and effective integration requires robust data protection, rigorous validation, bias mitigation strategies, interdisciplinary collaboration, clinical integration, and continuous ethical oversight. When responsibly governed, AI holds substantial promise for advancing personalized, equitable, and data-driven palliative care.
Abstract: Artificial intelligence (AI) is increasingly recognized as a transformative force in healthcare, with growing relevance in palliative care. This article examines the clinical potential, current applications, risks, and ethical preconditions associated with AI implementation in this sensitive field. AI-driven systems enhance personalized symptom man...
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Research Article
Exploring Equilibrium Perspectives Computational Mathematics Formula in Data Analytics and Artificial Intelligence Pipelines
Adeshola Raheem Kukoyi*,
Adedayo Hakeem Kukoyi
Issue:
Volume 1, Issue 1, March 2026
Pages:
49-56
Received:
8 December 2025
Accepted:
22 December 2025
Published:
12 March 2026
Abstract: In contemporary artificial intelligence (AI) and data analytics pipelines, phenomena such as data drift - characterized by shifts in input distributions - and model decay, defined as the progressive degradation of predictive performance due to evolving data patterns, pose significant threats to system reliability. The Kukoyi Formula, developed by Adeshola Raheem Kukoyi as 𝐾 = (0.230258509/2iπ) - 0.5, provides a novel computational framework grounded in systems theory to quantify equilibrium states in complex dynamical systems, leveraging the imaginary unit 'i' for modelling steady-state dynamics. This approach operationalizes AI pipelines through a K5 framework: K1 evaluates input quality via weighted metrics of accuracy, completeness, validity, and metadata; K2 assesses transformation integrity; K3 measures output performance; K4 computes equilibrium stability; and K5 gauges corrective feedback efficacy. The composite equilibrium index, K_(eq) = (K_1*K_2*K_3*K_4*K_5)^(0.2), yields scores categorizing system health: 0.80 – 1.00 (stable), 0.60 – 0.79 (moderate), 0.40 – 0.59 (fragile), and 0.00 – 0.39 (critical). Rooted in general systems theory, the formula addresses gaps in MLOps by integrating dynamic feedback loops, surpassing conventional monitoring in drift detection and adaptation. Hypothetical validation via a predictive maintenance case study demonstrates potential reduced downtime, enhanced model recalibration, and improved asset utilization through early equilibrium deviations. This study formalizes its deployment, investigates operationalization for coherence enhancement, anomaly mitigation, and superiority over benchmarks, while noting limitations in ultra-high-velocity contexts, advancing resilient AI pipelines.
Abstract: In contemporary artificial intelligence (AI) and data analytics pipelines, phenomena such as data drift - characterized by shifts in input distributions - and model decay, defined as the progressive degradation of predictive performance due to evolving data patterns, pose significant threats to system reliability. The Kukoyi Formula, developed by A...
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Research Article
Operational Security Assessment of Machine Learning Fraud Detection Systems: A Cybersecurity Perspective Using Stride, Explainability, and Anomaly Gating
Issue:
Volume 1, Issue 1, March 2026
Pages:
57-63
Received:
9 October 2025
Accepted:
28 February 2026
Published:
12 March 2026
Abstract: Machine learning (ML) enables large-scale analysis of transaction data and has become integral to financial fraud detection. Despite strong predictive performance, ML-based systems remain vulnerable to adversarial manipulation and are often insufficiently aligned with cybersecurity evaluation practices. This paper introduces an operational security assessment framework that shifts the focus from model optimisation toward holistic security evaluation. The framework combines STRIDE threat modelling to systematically identify vulnerabilities such as spoofing, tampering, and denial-of-service. It uses SHapley Additive exPlanations (SHAP) to embed contextual, SOC-ready alerts into Security Information and Event Management (SIEM) workflows and an anomaly-gating mechanism using Isolation Forest to assess resilience against adversarial and out-of-distribution samples. Using the IEEE-CIS dataset as a case study, the framework revealed susceptibility to identity spoofing, sensitivity to targeted feature perturbations and operational bottlenecks under simulated denial-of-service conditions. For Anomaly gating though it reduced false positives and captured adversarial manipulations it also imposed significant recall trade-offs, underscoring the challenge of balancing detection coverage with workload reduction. Embedding SHAP into structured alerts improved interpretability and supported drift-based anomaly identification. The study concludes that effective fraud detection requires moving beyond accuracy-centric evaluation toward integrated methodologies combining threat modelling, explainability and resilience testing. The proposed framework provides a structured blueprint for strengthening the operational security and trustworthiness of ML-driven fraud detection systems.
Abstract: Machine learning (ML) enables large-scale analysis of transaction data and has become integral to financial fraud detection. Despite strong predictive performance, ML-based systems remain vulnerable to adversarial manipulation and are often insufficiently aligned with cybersecurity evaluation practices. This paper introduces an operational security...
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