{"id":"W4412958808","doi":"10.4018/979-8-3373-1250-7.ch005","title":"Generative AI in Fraud Prevention","year":2025,"lang":"en","type":"book-chapter","venue":"Advances in computational intelligence and robotics book series","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Regina","funders":"","keywords":"Generative grammar; Computer science; Psychology; Artificial intelligence","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":false,"invisible_to_affiliation_only":false},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002611706,0.0008265096,0.0006392596,0.001992301,0.001329298,0.005464634,0.001437266,0.001696192,0.007743763],"category_scores_gemma":[0.007218715,0.0005082941,0.000916218,0.002133915,0.005451319,0.005296988,0.002895031,0.003691629,0.00327869],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002314712,"about_ca_system_score_gemma":0.001726687,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001377418,"about_ca_topic_score_gemma":0.001197441,"domain_scores_codex":[0.9981835,0.0006990712,0.00007694482,0.0002041846,0.0007168151,0.0001193756],"domain_scores_gemma":[0.9960669,0.00284269,0.0001157604,0.0005388055,0.0003386793,0.00009721487],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"theoretical_or_conceptual","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.00001379684,0.00003256123,0.0003025678,0.0001941904,0.00002316842,0.00006129768,0.0004585526,0.00906453,0.0003743022,0.8647906,0.01477462,0.1099098],"study_design_scores_gemma":[0.000007540784,0.00001468575,0.0001726754,0.0002228996,0.00001121978,0.000173795,0.000146276,0.02325994,0.0006735545,0.8195557,0.1557411,0.00002062933],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"other","genre_scores_codex":[0.009350654,0.05360499,0.579015,0.02037491,0.00222235,0.0002095138,0.0002327893,0.001356593,0.3336332],"genre_scores_gemma":[0.4538663,0.05769382,0.3169233,0.007657924,0.002430204,0.0004747832,0.0008449571,0.000882056,0.1592267],"genre_candidate":"other","genre_consensus":null,"teacher_disagreement_score":0.007743763,"threshold_uncertainty_score":0.02590549,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02382130938918915,"score_gpt":0.3153316160945524,"score_spread":0.2915103067053633,"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."}}