{"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":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0002557394,0.0001064899,0.0002235397,0.002588846,0.0001052466,0.00009543092,0.0001658422,0.00006939064,0.000002869248],"category_scores_gemma":[0.00008883564,0.00009015926,0.00003097114,0.005501388,0.000181548,0.0006472166,0.0001215446,0.0001776107,0.000005346169],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00002611944,"about_ca_system_score_gemma":0.00001306849,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0004234188,"about_ca_topic_score_gemma":0.0002509133,"domain_scores_codex":[0.9991242,0.000001829267,0.0002055229,0.0002994951,0.0001558168,0.000213178],"domain_scores_gemma":[0.999738,0.00003748952,0.00004974255,0.0001041237,0.00006297484,0.00000766805],"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":[0.000007107868,0.00003559317,0.05346448,0.0001342723,0.00007232138,0.00001721675,0.0002824953,0.2971802,0.01386485,0.6328626,0.000008202094,0.002070627],"study_design_scores_gemma":[0.00008429734,0.000009707398,0.01044771,0.00009326362,0.00006809766,8.772736e-7,0.0001042475,0.9862627,0.0006977816,0.0005977722,0.001539945,0.00009361433],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9930578,0.0006627491,0.00463959,0.0001795973,0.000207971,0.00007056925,0.000005359331,0.0003660529,0.0008103405],"genre_scores_gemma":[0.9996154,0.00003655033,0.0002628638,0.000003112993,0.00004666735,0.00001145954,0.000006579895,0.000005283887,0.00001205802],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6890824,"threshold_uncertainty_score":0.3676588,"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."}}