{"id":"W4205556360","doi":"10.3390/jrfm15010035","title":"Bankruptcy Prediction Using Machine Learning Techniques","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":94,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Solvency; Support vector machine; Artificial intelligence; Machine learning; Artificial neural network; Computer science; Bankruptcy; Bankruptcy prediction; Extreme learning machine; Boosting (machine learning); Gradient boosting; Data mining; Finance; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001282252,0.0007904826,0.0007264997,0.002848598,0.0002386391,0.0007420753,0.000469708,0.0007205289,0.0009206263],"category_scores_gemma":[0.003019178,0.0002007467,0.0005720704,0.001419753,0.0001989631,0.0005761739,0.0003942352,0.0007176026,0.0004494157],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0003852628,"about_ca_system_score_gemma":0.0005016749,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.005755709,"about_ca_topic_score_gemma":0.004607365,"domain_scores_codex":[0.9994466,0.000194593,0.00005286968,0.00009864882,0.0001387199,0.00006857423],"domain_scores_gemma":[0.9986312,0.0007704645,0.0002443467,0.00006402603,0.000240087,0.0000497959],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0002171132,0.0004666354,0.1049521,0.000120013,0.0002879917,0.000288195,0.00006786931,0.6345785,0.002302208,0.001113822,0.003244712,0.2523608],"study_design_scores_gemma":[0.000008777575,0.00004534105,0.0116657,0.00002140559,0.00001925512,0.00003269034,0.00001568488,0.9866332,0.0005588356,0.0006789613,0.0003118741,0.000008287861],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7399248,0.003111043,0.2485491,0.0008482975,0.0001477282,0.0001192927,0.000911364,0.001438035,0.004950416],"genre_scores_gemma":[0.9768921,0.0003471964,0.02141676,0.00005462044,0.00006081472,0.00002507129,0.0005862864,0.000008671914,0.0006084773],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.005755709,"threshold_uncertainty_score":0.01144445,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.007852221872601981,"score_gpt":0.1961759184842889,"score_spread":0.188323696611687,"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."}}