{"id":"W4393435531","doi":"10.54097/10dk2m95","title":"Predicting Loan Default: A Comparative Analysis of Multiple Machine Learning Models","year":2024,"lang":"en","type":"article","venue":"Highlights in Science Engineering and Technology","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":3,"is_retracted":false,"has_abstract":true,"ca_institutions":"University of Toronto","funders":"","keywords":"Loan; Computer science; Default; Non-performing loan; Artificial intelligence; Machine learning; Business; Finance","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":[0.01461253,0.002046109,0.00219564,0.006807022,0.0006106758,0.002822633,0.001673557,0.001640882,0.0008217797],"category_scores_gemma":[0.02066188,0.0004090698,0.002063575,0.003802826,0.000439182,0.002817862,0.001191384,0.001476818,0.0003106573],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001588623,"about_ca_system_score_gemma":0.001327759,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01171532,"about_ca_topic_score_gemma":0.008021121,"domain_scores_codex":[0.9951492,0.002462278,0.0003774946,0.0006517834,0.001094021,0.0002652362],"domain_scores_gemma":[0.9793702,0.01730128,0.0007877189,0.0006807972,0.001529326,0.0003307559],"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":[0.001365083,0.0008495849,0.1366638,0.0007953719,0.002195802,0.0003516267,0.0002363756,0.6154623,0.0009403604,0.004420487,0.004376655,0.2323426],"study_design_scores_gemma":[0.00002358801,0.000360227,0.01530884,0.0001025011,0.0002604601,0.00007010062,0.0001541394,0.9805514,0.000590764,0.00163122,0.000905773,0.00004092561],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8419451,0.0266914,0.1125427,0.003548903,0.0004218645,0.0002889711,0.001766009,0.001818628,0.01097653],"genre_scores_gemma":[0.9621449,0.003349577,0.03203899,0.0001418981,0.0001503394,0.00009246785,0.001175468,0.00008214934,0.0008242851],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01461253,"threshold_uncertainty_score":0.07727933,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01465539964691913,"score_gpt":0.2212831111141069,"score_spread":0.2066277114671878,"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."}}