{"id":"W2572843734","doi":"10.5539/ass.v13n2p176","title":"The Application of Discriminant Model in Managing Credit Risk for Consumer Loans in Vietnamese Commercial Bank","year":2017,"lang":"en","type":"article","venue":"Asian Social Science","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Loan; Vietnamese; Salary; Discriminant function analysis; Linear discriminant analysis; Variables; Actuarial science; Business; Demographic economics; Economics; Econometrics; Statistics; Finance; Mathematics","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":["sts"],"consensus_categories":[],"category_scores_codex":[0.0008115775,0.00007314645,0.0001149639,0.00008213737,0.001484585,0.0002489788,0.0005644814,0.00003779101,9.857554e-7],"category_scores_gemma":[0.000207379,0.0000583537,0.00004239825,0.0002343751,0.0006071265,0.0008656165,0.000146358,0.00008664998,0.0000029284],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004774896,"about_ca_system_score_gemma":0.00004272613,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002907902,"about_ca_topic_score_gemma":0.006508982,"domain_scores_codex":[0.9991299,0.000005336467,0.0002011847,0.0002007961,0.0002099832,0.0002528073],"domain_scores_gemma":[0.9993978,0.00001493855,0.0003031004,0.000198419,0.00007952526,0.000006258112],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.00005133497,0.00005694714,0.07515208,0.00003918396,0.000001579643,4.672825e-7,0.0005493258,0.00002685138,0.0002232442,0.2101734,0.0003070624,0.7134185],"study_design_scores_gemma":[0.0003272098,0.000003361572,0.8996428,0.00002294391,0.00001193009,3.331922e-8,0.0006449222,0.06687714,0.00001474655,0.03064936,0.001720771,0.00008474867],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7633416,0.00007921306,0.03866556,0.01010236,0.001159336,0.00180944,0.00006738544,0.00005712685,0.184718],"genre_scores_gemma":[0.9995258,0.00001358061,0.00006332426,0.00005300393,0.0002529985,0.00006532793,0.000003760786,0.000005360914,0.0000167859],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.8244907,"threshold_uncertainty_score":0.9998153,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01969203573056471,"score_gpt":0.2816543784121787,"score_spread":0.2619623426816139,"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."}}