{"id":"W4361186564","doi":"10.1117/12.2672657","title":"Comparison of different individual credit risk assessment models","year":2023,"lang":"en","type":"article","venue":"","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Alberta","funders":"","keywords":"Credit risk; Logistic regression; Random forest; Gradient boosting; Boosting (machine learning); Machine learning; Credit score; Computer science; Artificial intelligence; Regression; Econometrics; Actuarial science; Statistics; Mathematics; 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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.004435555,0.001101589,0.0009908458,0.003120419,0.0003974856,0.001696129,0.001113804,0.0009303329,0.001641356],"category_scores_gemma":[0.007955437,0.0002258834,0.001495156,0.001779515,0.0003630506,0.001719239,0.000790248,0.0008647994,0.0003848035],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00117772,"about_ca_system_score_gemma":0.001083647,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01333234,"about_ca_topic_score_gemma":0.00571044,"domain_scores_codex":[0.9985344,0.0005080676,0.0001226886,0.0002522215,0.0003987815,0.0001837886],"domain_scores_gemma":[0.9958386,0.00246279,0.0003078928,0.0002746474,0.0009187862,0.0001971948],"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":[0.0006162904,0.0003456855,0.07810698,0.0001860843,0.0004223672,0.0001384692,0.000192849,0.7286071,0.0006480361,0.004120479,0.004451477,0.1821642],"study_design_scores_gemma":[0.00001375484,0.0001010958,0.01021756,0.00003617749,0.00008076268,0.00006299883,0.00008386801,0.9858912,0.0004682946,0.002337611,0.000681887,0.00002485411],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7943676,0.003728914,0.184632,0.001495938,0.0001920783,0.0001934208,0.001232089,0.001668988,0.01248905],"genre_scores_gemma":[0.9783127,0.000633424,0.01840964,0.00007280547,0.0000430081,0.00006304437,0.0008015811,0.00003905408,0.001624647],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01333234,"threshold_uncertainty_score":0.02650946,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05758705035982953,"score_gpt":0.3030497812997123,"score_spread":0.2454627309398828,"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."}}