{"id":"W4406203384","doi":"10.61091/jcmcc123-24","title":"Deep Learning Model Based Research on Anomaly Detection and Financial Fraud Identification in Corporate Financial Reporting Statements","year":2024,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Financial statement; Anomaly detection; Audit; Artificial neural network; Autoencoder; Accounting; Artificial intelligence; Computer science; Financial ratio; Anomaly (physics); Identification (biology); F1 score; Machine learning; Finance; Data mining; Business","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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.008662095,0.0002264486,0.0004820692,0.0008520704,0.0003837298,0.0008680283,0.0004809358,0.00019201,7.469809e-7],"category_scores_gemma":[0.00312378,0.000222115,0.00007783066,0.001016247,0.00008519908,0.0008250467,0.0002528426,0.001165794,0.000001636908],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0002448257,"about_ca_system_score_gemma":0.00036418,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000008579627,"about_ca_topic_score_gemma":0.000001482698,"domain_scores_codex":[0.9959694,0.0002774472,0.002010945,0.000449117,0.0009265186,0.0003666217],"domain_scores_gemma":[0.9961922,0.0006180736,0.002118872,0.0003076692,0.0006408824,0.0001223508],"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":[0.00008510327,0.0004123066,0.0002624668,0.0003937382,0.00001798929,0.0001185281,0.001660576,0.002571651,0.008111841,0.9416048,0.00008282379,0.04467816],"study_design_scores_gemma":[0.0007028776,0.0004007663,0.0005889179,0.0004366387,0.000008310319,0.00002143268,0.00004277474,0.6067991,0.003183716,0.3875931,0.00007341254,0.0001489789],"study_design_candidate":"theoretical_or_conceptual","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4089837,0.0001212286,0.5871869,0.0001029064,0.003192766,0.0002485875,8.02344e-7,0.00008359877,0.00007949312],"genre_scores_gemma":[0.9835575,0.00003615966,0.01600225,0.00001633699,0.0003481414,0.000009055519,0.000001843747,0.00002291699,0.000005840996],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6042274,"threshold_uncertainty_score":0.9057587,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.08164214882031957,"score_gpt":0.3573638843522708,"score_spread":0.2757217355319513,"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."}}