{"id":"W4402547213","doi":"10.1016/j.ejor.2024.09.025","title":"Attention-based dynamic multilayer graph neural networks for loan default prediction","year":2024,"lang":"en","type":"article","venue":"European Journal of Operational Research","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":38,"is_retracted":false,"has_abstract":true,"ca_institutions":"Western University","funders":"Economic and Social Research Council; Alliance de recherche numérique du Canada; Icelandic Centre for Research; Natural Sciences and Engineering Research Council of Canada; Canada Research Chairs","keywords":"Computer science; Loan; Artificial neural network; Graph; Artificial intelligence; Econometrics; Machine learning; Finance; Business; Economics; Theoretical computer science","routes":{"ca_aff":true,"ca_fund":true,"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":[0.002807173,0.0001159714,0.0001174499,0.000562131,0.0004214592,0.0009375271,0.0002347284,0.00003277712,0.0001179593],"category_scores_gemma":[0.0003131092,0.00009445425,0.0001851103,0.0005134928,0.00007725027,0.001075206,0.00004883754,0.0004385946,0.00005798746],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00006447979,"about_ca_system_score_gemma":0.0000747638,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00001507305,"about_ca_topic_score_gemma":0.00001362898,"domain_scores_codex":[0.9982751,0.0001022164,0.0004657655,0.0002056404,0.0006858229,0.0002654316],"domain_scores_gemma":[0.9984136,0.0001391283,0.00009437603,0.00009856321,0.001227778,0.00002661991],"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":[0.001842779,0.0007248952,0.01321775,0.001004366,0.0003023106,0.0006960155,0.0001210664,0.6032208,0.01161843,0.03115749,0.1559601,0.180134],"study_design_scores_gemma":[0.0005989079,0.0001141023,0.06511914,0.0001918554,0.00002733182,0.000009828567,0.00003081393,0.9032468,0.000006910827,0.0002294784,0.03033803,0.00008681173],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5669361,0.001636642,0.4143656,0.006459253,0.004715209,0.0008430672,0.00008569562,0.0001484795,0.00480991],"genre_scores_gemma":[0.9957426,0.00001921421,0.0002658456,0.0002034417,0.00326903,0.00001081796,0.0001421135,0.00003876947,0.0003082239],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.4288065,"threshold_uncertainty_score":0.9040595,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04369009283269483,"score_gpt":0.3155599658831095,"score_spread":0.2718698730504147,"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."}}