{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.003308914,0.0003357073,0.0002203976,0.001010169,0.0002596826,0.001533956,0.0004940475,0.0004376998,0.000890175],"category_scores_gemma":[0.008288486,0.0001156459,0.0004054814,0.001265871,0.0005033555,0.002276272,0.0008046178,0.0008411959,0.000137557],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009515125,"about_ca_system_score_gemma":0.001034973,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01641193,"about_ca_topic_score_gemma":0.01758643,"domain_scores_codex":[0.9991886,0.0004194454,0.00005025826,0.00006227897,0.0002031967,0.00007617099],"domain_scores_gemma":[0.9934213,0.003412793,0.0009345834,0.0003363206,0.001631937,0.0002631371],"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":[0.0008028058,0.001067175,0.661207,0.0006922919,0.0006096252,0.0008077704,0.001357102,0.03957387,0.0008265949,0.01357583,0.004036101,0.2754438],"study_design_scores_gemma":[0.0001767626,0.0008790572,0.6339371,0.0009949958,0.0006400408,0.0002823406,0.007254941,0.3222606,0.004200999,0.01711955,0.0121597,0.00009397858],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9822159,0.002672483,0.004631374,0.003219804,0.00002879972,0.00005656982,0.0002517478,0.00003283559,0.006890432],"genre_scores_gemma":[0.9966844,0.001158526,0.001499368,0.00008500314,0.00001441551,0.000007241643,0.0001213982,0.000002801448,0.0004268414],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01641193,"threshold_uncertainty_score":0.03263283,"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."}}