{"id":"W7161307961","doi":"10.1145/3800000.3800105","title":"Machine Learning for Corporate Default Risk: Improving Prediction Accuracy in an Era of Globalization and Digitalization","year":2025,"lang":"","type":"article","venue":"","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"","keywords":"Interpretability; Random forest; Credit risk; Gradient boosting; Financial risk; Predictive modelling; Predictive power; Financial risk management; Logistic regression; Boosting (machine learning)","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":["metaepi_narrow"],"consensus_categories":[],"category_scores_codex":[0.0005490479,0.0002758539,0.0003399804,0.0003781958,0.0003847675,0.0005817345,0.0001260135,0.0002162882,0.00002910603],"category_scores_gemma":[0.001221562,0.0002896423,0.00006994385,0.001136207,0.00007236088,0.003924703,0.0001404207,0.0002171697,0.000001262974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00007556544,"about_ca_system_score_gemma":0.00006588351,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003573366,"about_ca_topic_score_gemma":0.001760454,"domain_scores_codex":[0.9980828,0.00004303646,0.0008724627,0.0005237293,0.0001941386,0.0002838286],"domain_scores_gemma":[0.9979218,0.00007722274,0.001238581,0.0001735147,0.0005728995,0.00001595353],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0003596164,0.0002143949,0.7743726,0.001378337,0.00002376384,5.120294e-7,0.00005973715,0.01040484,0.0004270815,0.02061713,0.00007799601,0.1920639],"study_design_scores_gemma":[0.001341966,0.00007807198,0.3390802,0.0003751107,0.0001547231,1.669178e-7,0.0001675741,0.6538865,0.00008608085,0.003806692,0.0008662743,0.00015665],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.6864094,0.0008917017,0.3086976,0.0001172446,0.0007277658,0.001195189,0.0001304846,0.0001255049,0.001705078],"genre_scores_gemma":[0.9974396,0.000571063,0.0001719883,0.00007898736,0.0002103442,0.00004289389,0.001285823,0.00002454521,0.0001748095],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6434817,"threshold_uncertainty_score":0.9999556,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01182047192751281,"score_gpt":0.2304991131904454,"score_spread":0.2186786412629325,"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."}}