{"id":"W7077046543","doi":"10.33423/jabe.v27i4.7783","title":"Artificial Intelligence Applications and the Impact on Banking Operations","year":2025,"lang":"en","type":"article","venue":"Journal of Applied Business and Economics","topic":"Water Quality and Resources Studies","field":"Environmental Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Corporate governance; Banking industry; Applications of artificial intelligence; Retail banking; Financial services","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":[0.002530624,0.0001933202,0.0001208682,0.001466847,0.001232561,0.005153117,0.0004209982,0.0008791382,0.006123248],"category_scores_gemma":[0.01543692,0.0001314824,0.0001609796,0.002210246,0.002070599,0.00221722,0.002336892,0.001002427,0.0006794173],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001838354,"about_ca_system_score_gemma":0.001805624,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003168131,"about_ca_topic_score_gemma":0.003795828,"domain_scores_codex":[0.9954457,0.002457835,0.0001997221,0.0001643389,0.001206421,0.0005260646],"domain_scores_gemma":[0.9768602,0.01406848,0.003995175,0.0005087247,0.003144359,0.001423066],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0003614237,0.0009146432,0.4610628,0.0006043353,0.00009309477,0.002097734,0.0180452,0.01164902,0.002837103,0.1070661,0.01322515,0.3820434],"study_design_scores_gemma":[0.00004621318,0.0007148104,0.6242563,0.001523959,0.0001556316,0.002130551,0.06655288,0.02139006,0.003810138,0.1175705,0.1617312,0.0001177018],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8434874,0.004206023,0.002576797,0.01457053,0.00008387282,0.0000392672,0.00008727075,0.00005444093,0.1348943],"genre_scores_gemma":[0.9958605,0.001593283,0.0004907669,0.0002923434,0.00003996735,0.000007554529,0.00001910138,0.000006731516,0.001689701],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.006123248,"threshold_uncertainty_score":0.02048427,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02970739691094959,"score_gpt":0.247911164365793,"score_spread":0.2182037674548434,"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."}}