{"id":"W4392249562","doi":"10.4236/ti.2024.151005","title":"Key Trends Driving Adoption of Generative Artificial Intelligence in Malaysian Banking Sectors","year":2024,"lang":"en","type":"article","venue":"Technology and Investment","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Key (lock); Generative grammar; Business; Computer science; Artificial intelligence; Computer security","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001386533,0.0001147027,0.0001032172,0.000962733,0.001151543,0.002702965,0.0003666955,0.0004334765,0.001903612],"category_scores_gemma":[0.004887956,0.0002111398,0.0001173377,0.001344793,0.001016292,0.001438509,0.001211196,0.0008479196,0.0004347608],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002092595,"about_ca_system_score_gemma":0.003455336,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01655467,"about_ca_topic_score_gemma":0.02943929,"domain_scores_codex":[0.9984761,0.0003963945,0.0001926664,0.0001611413,0.0004015704,0.0003721169],"domain_scores_gemma":[0.9934048,0.001357106,0.002793555,0.0001625455,0.001217116,0.00106485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.00005475263,0.0001040223,0.8636132,0.0001726268,0.00001179135,0.0007959683,0.07625099,0.0002759979,0.00182455,0.003601593,0.001453267,0.05184128],"study_design_scores_gemma":[0.000002564129,0.0001025986,0.8200618,0.0002261423,0.00000983413,0.0005155146,0.1632789,0.0007457093,0.0007109507,0.0005310322,0.01379104,0.00002396157],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9934222,0.000247319,0.0001494395,0.001180101,0.000007221501,0.0000160028,0.00005223855,0.000003881948,0.004921575],"genre_scores_gemma":[0.998464,0.0003791943,0.0002163474,0.0001523501,0.000003720702,0.000008009805,0.00002499702,0.000001803533,0.0007495781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01655467,"threshold_uncertainty_score":0.03291661,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01967571224594273,"score_gpt":0.2404483499241526,"score_spread":0.2207726376782098,"validation_status":"score_only:v0-immature-baseline","note":"Baseline scores from an immature model (maturity gate not passed). Scores rank; they never assert a category."}}