{"id":"W4205468851","doi":"10.5539/ijef.v14n2p32","title":"The Integration of Big Data and Artificial Neural Networks for Enhancing Credit Risk Scoring in Emerging Markets: Evidence from Egypt","year":2022,"lang":"en","type":"article","venue":"International Journal of Economics and Finance","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Inefficiency; Big data; Emerging markets; Artificial neural network; Credit risk; Credit rating; Value (mathematics); Business; Economics; Actuarial science; Artificial intelligence; Computer science; Finance; Machine learning; Data mining; Microeconomics","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":[],"consensus_categories":[],"category_scores_codex":[0.001827594,0.00008805478,0.0002483347,0.000157072,0.0001709378,0.00009706195,0.0005232735,0.00002665106,0.000004228034],"category_scores_gemma":[0.0003311372,0.00009086701,0.00005498659,0.00006915878,0.0000473048,0.0004217597,0.0002871648,0.0002100328,2.358157e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00008895525,"about_ca_system_score_gemma":0.00002355149,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.0003475465,"about_ca_topic_score_gemma":0.0008122189,"domain_scores_codex":[0.9986451,0.00002024027,0.000936111,0.0002290066,0.00003684111,0.000132735],"domain_scores_gemma":[0.9981833,0.0003338048,0.001233997,0.0001745524,0.00005896409,0.00001545485],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.001187655,0.00008332049,0.06612989,0.00001084089,0.0001606488,0.00001021433,0.001189897,0.1376849,0.00002702588,0.08174686,0.0002594132,0.7115093],"study_design_scores_gemma":[0.0005950308,0.000118399,0.05187402,0.00007970147,0.00001133442,0.000007358549,0.0004553644,0.8807564,0.00004020105,0.04721916,0.01869024,0.0001527559],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.964333,0.008910151,0.02250669,0.0008302697,0.002910401,0.0001229443,0.0003398399,0.000001173313,0.00004550903],"genre_scores_gemma":[0.9650722,0.03363075,0.0006634733,0.00005000559,0.0005362981,0.00001169705,0.00001060194,0.000008905967,0.00001608712],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.7430716,"threshold_uncertainty_score":0.3705449,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06302920720174862,"score_gpt":0.2605865591195212,"score_spread":0.1975573519177725,"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."}}