{"id":"W3177284477","doi":"10.1109/saci51354.2021.9465561","title":"Using Machine Learning Algorithms to create a Credit Scoring Model for mobile money users","year":2021,"lang":"en","type":"article","venue":"","topic":"Financial Distress and Bankruptcy Prediction","field":"Business, Management and Accounting","cited_by":1,"is_retracted":false,"has_abstract":true,"ca_institutions":"McGill University","funders":"","keywords":"Computer science; Cluster analysis; Database transaction; Credit card; Machine learning; Payment; Financial institution; Loan; Credit risk; Artificial intelligence; Data mining; Finance; Business; Database","routes":{"ca_aff":true,"ca_fund":false,"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":[0.0001396408,0.0001699302,0.000199122,0.0001334474,0.0003555382,0.0003207634,0.0001084062,0.00006292217,0.000107373],"category_scores_gemma":[0.0001165147,0.0001687932,0.0001090822,0.0003658212,0.00001239471,0.0007309874,0.0001766042,0.0001074625,0.00002430732],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00004393758,"about_ca_system_score_gemma":0.0000358292,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.001369688,"about_ca_topic_score_gemma":0.0002163739,"domain_scores_codex":[0.9988841,0.000004210555,0.0002345472,0.0003661743,0.0001808794,0.0003300863],"domain_scores_gemma":[0.9994892,0.00001589983,0.00008584219,0.0001533111,0.0002337744,0.0000219773],"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":[0.0001710437,0.0002220126,0.01468269,0.0004651489,0.00007507233,0.00003486554,0.0001923271,0.9167891,0.01864699,0.009551324,0.002897462,0.03627201],"study_design_scores_gemma":[0.0004069272,0.0000127246,0.0004180032,0.00007922978,0.00005865422,0.000001261714,0.0001550744,0.9852454,0.0008763623,0.0004568063,0.01207038,0.0002191711],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4520696,0.0001211343,0.5423141,0.0001686064,0.0005816061,0.0004552997,0.00003617179,0.0003049544,0.003948531],"genre_scores_gemma":[0.9709063,0.0000208227,0.02141131,0.0009099247,0.001714786,0.0001488882,0.0001925155,0.00006855042,0.004626935],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.5209028,"threshold_uncertainty_score":0.6883187,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04611775782548326,"score_gpt":0.2675054701922915,"score_spread":0.2213877123668083,"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."}}