{"id":"W4396903166","doi":"10.5430/afr.v13n2p107","title":"Machine Learning in Credit Risk Forecasting —— A Survey on Credit Risk Exposure","year":2024,"lang":"en","type":"article","venue":"Accounting and Finance Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Credit risk; Business; Actuarial science; Finance","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":["scholarly_communication"],"consensus_categories":[],"category_scores_codex":[0.006776001,0.0002518221,0.0002891053,0.0007921592,0.0009114151,0.001145177,0.0002643212,0.0001833246,0.00004129826],"category_scores_gemma":[0.003788922,0.0002285712,0.00007175449,0.00183266,0.0001315663,0.001067882,0.000301358,0.001944958,0.0001548301],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0000696518,"about_ca_system_score_gemma":0.00005751481,"about_ca_topic_candidate":true,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.02801221,"about_ca_topic_score_gemma":0.003366297,"domain_scores_codex":[0.9972391,0.0001390566,0.0004144251,0.0007300942,0.000680049,0.0007973318],"domain_scores_gemma":[0.9985416,0.0007718174,0.0001670945,0.0002373928,0.0002679632,0.0000141439],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.0001373803,0.00006084744,0.7609903,0.0002393818,0.00001408674,0.00007948728,0.0001084863,0.0008409396,0.00001510811,0.002306648,0.003428279,0.2317791],"study_design_scores_gemma":[0.0003919868,0.00007411143,0.7075412,0.0006763283,0.00001410194,0.000002271564,0.00009055159,0.232167,0.000006128585,0.002187112,0.05660482,0.0002444317],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9909567,0.003223218,0.0001240512,0.0001890116,0.0006252427,0.0002593211,0.0000739114,0.000198592,0.004349944],"genre_scores_gemma":[0.9947415,0.002396122,0.00003971254,0.00003618724,0.002087958,0.00005020546,0.0001121433,0.0000542704,0.0004819201],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.2315346,"threshold_uncertainty_score":0.9998918,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.05342851874066952,"score_gpt":0.2914325679995278,"score_spread":0.2380040492588583,"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."}}