Prashanth BS | Searcharxiv

arXiv2026

Technology Transfer Readiness, Explainable AI and Financial Innovation Capability Transitions in Expanded BRICS: Benchmarking Against Advanced Innovation Economies

This study examines the dynamics of technology transfer readiness and financial innovation capability transitions across the expanded BRICS economies, benchmarked against advanced innovation systems through explainable AI. Using a composite Innovation Capability Development-Readiness index (ICDI) constructed through principal component analysis, the paper evaluates the structural conditions enabling knowledge diffusion, industrial upgrading, and financial innovation ecosystem development. A Markov transition framework is employed to analyse how countries evolve across readiness tiers over time, capturing both persistence and mobility in innovation capabilities. The results reveal significant asymmetries in transition probabilities between advanced economies and emerging innovation systems, with several BRICS economies demonstrating gradual upgrading trajectories while others remain structurally locked in lower readiness states. These findings highlight the institutional and policy conditions required to strengthen technology transfer ecosystems. Successful countries in these areas attract foreign investment, participate in global value chains, and profit from technology partnerships. The study contributes to the literature on innovation capability formation and industrial transformation by integrating composite readiness measurement with dynamic transition modelling to inform evidence-based innovation policy.

econ.EM↗

arXiv2026

Prediction of bank transaction fraud using TabNet an adaptive deep learning architecture

The development of online banking has brought about an increase in fraudulent operations, which is a major problem for banks. This study delves into the urgent requirement for interpretable, scalable, and top-notch fraud detection systems by using TabNet, an adaptable deep learning framework, on a Kaggle dataset consisting of actual bank transactions in India. Maximizing operational risk management by improving the accuracy of transaction anomaly detection and ensuring regulatory compliance through transparent models is the goal. We utilize a supervised learning pipeline that incorporates the Synthetic Minority Oversampling Technique (SMOTE) to ensure that classes are balanced. Subsequently, we conduct thorough exploratory data analysis (EDA) to identify patterns of fraud, both during specific times and across behaviors. On this dataset, five different deep learning architectures are tested: DNN, GRU, LSTM, CNN1D, and TabNet. Assessment of predictive performance was carried out using a 3-fold cross-validation framework. With a ROC-AUC of 0.9739 and an accuracy of 97.39 %, TabNet considerably outperformed the competition. The method of sparse feature selection used improved interpretability, generalized better on tabular data, and produced fewer false positives and negatives. Critical insights for operational fraud detection systems and a contribution to the broader literature on explainable AI (XAI) in financial decision-making are offered by the findings. Goals 8 and 16 of the Sustainable Development Agenda are supported by this study, which promotes inclusive economic growth and institutional transparency. Supporting strong, policy-compliant, and interpretable decision-support systems, it also offers practical use for real-time implementation in banking infrastructure.

q-fin.GN↗