{"id":"W3199888448","doi":"10.1109/ijcnn52387.2021.9533313","title":"Multi-Class Mobile Money Service Financial Fraud Detection by Integrating Supervised Learning with Adversarial Autoencoders","year":2021,"lang":"en","type":"article","venue":"","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université de Montréal; Concordia University","funders":"Coordenação de Aperfeiçoamento de Pessoal de Nível Superior","keywords":"Computer science; Autoencoder; Artificial intelligence; Machine learning; Database transaction; Anomaly detection; Credit card fraud; Financial services; Audit; Deep learning; Payment; Credit card; Finance; Accounting","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":[0.001900432,0.0007155232,0.0006889041,0.0007588776,0.0002698684,0.0006405343,0.0008471507,0.0006146286,0.000530663],"category_scores_gemma":[0.002868626,0.0003114859,0.0005169491,0.0003575783,0.0005809407,0.0008718196,0.0008954058,0.00103684,0.0002201212],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0006307876,"about_ca_system_score_gemma":0.0005042256,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.002263639,"about_ca_topic_score_gemma":0.002067232,"domain_scores_codex":[0.9992977,0.0002406993,0.00003825068,0.0001280898,0.0001952416,0.00009998314],"domain_scores_gemma":[0.9986318,0.0006605832,0.0001870396,0.0001651718,0.0002891114,0.0000662574],"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":[0.0002906982,0.0003137191,0.005075622,0.00003975341,0.0001677859,0.0001192059,0.00008330768,0.781513,0.00716056,0.003549106,0.001165582,0.2005217],"study_design_scores_gemma":[0.000001125821,0.00001100074,0.0001624775,0.000001313264,0.000002993582,0.000006579423,0.000002136148,0.9988019,0.0006494371,0.0003111601,0.0000480596,0.000001763253],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.1816015,0.0003181804,0.8149131,0.0003212079,0.00007387049,0.00007323051,0.00004823758,0.0008931084,0.001757452],"genre_scores_gemma":[0.9333503,0.00009454713,0.06496175,0.00009257652,0.00004333251,0.00003443556,0.00009508697,0.00002506311,0.00130291],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.002263639,"threshold_uncertainty_score":0.01005059,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01044714421245992,"score_gpt":0.2301160597285519,"score_spread":0.2196689155160919,"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."}}