{"id":"W4386952443","doi":"10.1109/iceccme57830.2023.10253185","title":"An Efficient Resampling Technique for Financial Statements Fraud Detection: A Comparative Study","year":2023,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Ottawa","funders":"","keywords":"Financial statement; Computer science; Artificial neural network; Audit; Machine learning; Financial ratio; Artificial intelligence; Benchmark (surveying); Metric (unit); Finance; Resampling; Accounting; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0008724632,0.0001375147,0.0001689226,0.0002636132,0.0003037819,0.0001574234,0.0008410645,0.00005173388,0.000004921853],"category_scores_gemma":[0.00005819003,0.0001285705,0.00003469384,0.0009937648,0.00002744061,0.0004490388,0.0001839875,0.0001018221,0.00004232571],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008211235,"about_ca_system_score_gemma":0.00006546777,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00002301105,"about_ca_topic_score_gemma":0.0000342834,"domain_scores_codex":[0.9984854,0.00008626131,0.0003189784,0.0005523134,0.0002713325,0.0002856439],"domain_scores_gemma":[0.9987229,0.0001030905,0.0001107943,0.0008048249,0.0001918151,0.00006659616],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"bench_or_experimental","study_design_gemma":"bench_or_experimental","study_design_scores_codex":[0.0004021099,0.006662826,0.002242081,0.0001643558,0.0001526983,0.00004549788,0.02532867,0.01175798,0.5954406,0.23044,0.0288156,0.09854764],"study_design_scores_gemma":[0.0009476881,0.001922807,0.01137818,0.00002278474,0.000008567244,0.000003361704,0.001090087,0.420139,0.5495095,0.007809089,0.006634894,0.0005339524],"study_design_candidate":"bench_or_experimental","study_design_consensus":"bench_or_experimental","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.02484222,0.000001529366,0.9707019,0.00009313674,0.0001646717,0.002305737,0.00002329905,0.001717175,0.0001503516],"genre_scores_gemma":[0.8415568,8.062387e-7,0.1563664,0.00006131239,0.00003462574,0.001869364,0.00002123181,0.000008329561,0.00008115802],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8167146,"threshold_uncertainty_score":0.5242953,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09714873008454457,"score_gpt":0.4194438881633981,"score_spread":0.3222951580788535,"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."}}