{"id":"W6969000033","doi":"10.5281/zenodo.6501987","title":"Practical Difficulties and Applications of GNNs on Financial Data for Fraud Detection","year":2022,"lang":"en","type":"article","venue":"Zenodo (CERN European Organization for Nuclear Research)","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Larus Technologies (Canada)","funders":"","keywords":"Database transaction; Big data; Key (lock); Financial fraud; Transaction data","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":[0.006983595,0.0008895925,0.0009598196,0.003197371,0.001414203,0.002215095,0.00128724,0.002508819,0.001547535],"category_scores_gemma":[0.03782672,0.0005295835,0.0005522143,0.003049819,0.002327817,0.004678437,0.002467389,0.002696679,0.0005024442],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00215464,"about_ca_system_score_gemma":0.0009167901,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006375833,"about_ca_topic_score_gemma":0.005425927,"domain_scores_codex":[0.9964509,0.002066943,0.0001886504,0.0005484332,0.0005610022,0.0001840372],"domain_scores_gemma":[0.9806466,0.01399549,0.001006582,0.002038523,0.001844438,0.0004683969],"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":[0.0006671075,0.0002388,0.03675424,0.0002927405,0.0001674334,0.000758091,0.0008863473,0.5014222,0.004484652,0.08743822,0.01341026,0.3534799],"study_design_scores_gemma":[0.00001571351,0.00002520023,0.001425331,0.0000379535,0.00001371089,0.0001792676,0.0002233231,0.8968593,0.001356573,0.09704394,0.002804947,0.00001465435],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2980824,0.004082032,0.671528,0.0154121,0.0006262627,0.0002115245,0.001058049,0.00131058,0.007689027],"genre_scores_gemma":[0.8514512,0.001020074,0.1440095,0.0004816003,0.0002871875,0.00007967876,0.0006181715,0.00009632952,0.001956221],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006983595,"threshold_uncertainty_score":0.03693318,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06599477865940498,"score_gpt":0.296637129761727,"score_spread":0.230642351102322,"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."}}