{"id":"W4312127937","doi":"10.3390/jrfm15120597","title":"The Value of Open Banking Data for Application Credit Scoring: Case Study of a Norwegian Bank","year":2022,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":18,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Norges Forskningsråd","keywords":"Credit score; Profitability index; Computer science; Database transaction; Norwegian; Credit rating; Transaction data; Credit history; Predictive modelling; Business; Machine learning; Artificial intelligence; Finance; Database","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002986209,0.0007548026,0.0004628687,0.001152131,0.0007968275,0.001469419,0.0009472732,0.001206613,0.001206183],"category_scores_gemma":[0.008565838,0.0002479493,0.0004453817,0.001773414,0.001137621,0.001648587,0.001008091,0.001305875,0.0003382289],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002195603,"about_ca_system_score_gemma":0.001287753,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.06250115,"about_ca_topic_score_gemma":0.07300398,"domain_scores_codex":[0.9986899,0.0006361037,0.00009443815,0.0001469543,0.00030242,0.0001301451],"domain_scores_gemma":[0.9906854,0.00633323,0.0005559511,0.0006227614,0.001150756,0.0006518167],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"observational","study_design_scores_codex":[0.002249574,0.003475064,0.4504027,0.0004269332,0.0003123237,0.010758,0.003599165,0.348776,0.00457249,0.005312613,0.02578722,0.144328],"study_design_scores_gemma":[0.0001944102,0.0007802552,0.1421752,0.0001451515,0.0001466123,0.001522595,0.003941507,0.8282939,0.00665767,0.005546638,0.0104801,0.0001158971],"study_design_candidate":"observational","study_design_consensus":"observational","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9926386,0.0003502255,0.002594417,0.001108282,0.00003521712,0.00004679667,0.0007642829,0.0001288193,0.002333239],"genre_scores_gemma":[0.9945611,0.0002030426,0.003385365,0.0000803207,0.00002545747,0.0000178697,0.0006764423,0.00001807491,0.001032284],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.06250115,"threshold_uncertainty_score":0.1242747,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02584519731767793,"score_gpt":0.2669887423584239,"score_spread":0.241143545040746,"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."}}