@article{CRSIJ26000275,
                  author = {Ayadi Moniaye},
                  title = {AI-Assisted Digital Intensity Index for Nigerian Deposit Money Banks (2000–2024): Construction, Validation, and Tier-Based Evidence.},
                  journal = {Cosmo Research and Science International Journal},
                  year = {2025},
                  volume = {2},
                  number = {1},
                  pages = {322-335},
                  issn = {3108-1584},
                  url = {https://cosmorsij.com/published/CRSIJ26000275.pdf},
                  abstract = {This study develops and validates an AI-assisted Digital Intensity Index (DII) for Nigerian Deposit Money Banks (DMBs) over 2000–2024. The index captures bank-level digital transformation across four dimensions: digital infrastructure, digital usage, digital innovation, and digital governance. Using bank disclosures and regulatory data, the study applies z-score normalization and hybrid, data-driven weighting combining Principal Component Analysis (PCA), entropy methods, and machine-learning feature importance. Where early-period data are incomplete, AI-assisted reconstruction is employed using supervised learning trained on observed subsamples. Reliability, construct validity, and robustness are assessed using Cronbach’s alpha, factor analysis, rank concordance, and alternative scaling schemes. Results show strong internal consistency, scale invariance, and persistent digital leadership by internationally licensed (Tier-1) banks relative to nationally licensed (Tier-2) banks. The study provides a transparent, longitudinal tool for benchmarking bank digital transformation in emerging markets.},
                  keywords = {Artificial Intelligence (AI), Deposit Money Banks, Digital Intensity Index (DII), Digital Transformation, Machine Learning, Nigeria Principal Component Analysis (PCA) Tier-Based Banking},
                  month = {July}
        }