{"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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.001286351,0.0006362609,0.0006338657,0.001090423,0.000479638,0.001122171,0.0009475618,0.00117823,0.002123366],"category_scores_gemma":[0.004259157,0.0003030179,0.0005445402,0.0009462017,0.0003354223,0.00113693,0.0004907018,0.001432232,0.001040831],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009802138,"about_ca_system_score_gemma":0.0008021283,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01368875,"about_ca_topic_score_gemma":0.0103464,"domain_scores_codex":[0.9996258,0.0001206303,0.00003223698,0.00008822035,0.00007071871,0.00006240958],"domain_scores_gemma":[0.9987825,0.0006591696,0.0001056354,0.00006415566,0.0003434927,0.00004510528],"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":[0.0001487158,0.0002114485,0.01471994,0.00003117464,0.00006510125,0.0001549177,0.0001180459,0.8022712,0.001084776,0.003903076,0.002123817,0.1751677],"study_design_scores_gemma":[0.000001722855,0.000008240324,0.0002976648,0.00000228219,0.000002237423,0.000008125027,0.00000757047,0.9987082,0.0001200983,0.0007243319,0.0001169356,0.000002634064],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1839519,0.0002128591,0.8097211,0.0007100503,0.00009577619,0.0002146851,0.0003264947,0.001157328,0.00360984],"genre_scores_gemma":[0.856522,0.0001420012,0.1378366,0.0001301416,0.00005466839,0.0002233179,0.0004165533,0.00004331172,0.004631537],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01368875,"threshold_uncertainty_score":0.0272181,"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."}}