{"id":"W6950550149","doi":"10.5281/zenodo.6501988","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":"Geophysics and Gravity Measurements","field":"Earth and Planetary Sciences","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":[0.008246539,0.0009967354,0.001054505,0.002908431,0.001505666,0.002974256,0.00170108,0.003322256,0.002209932],"category_scores_gemma":[0.04993478,0.0006645971,0.0006150487,0.003048139,0.002896256,0.006651218,0.003269217,0.002990793,0.000553073],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.002484663,"about_ca_system_score_gemma":0.001195555,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008863359,"about_ca_topic_score_gemma":0.006372311,"domain_scores_codex":[0.9941758,0.003660992,0.0002491373,0.0007740351,0.0008734602,0.0002665718],"domain_scores_gemma":[0.967622,0.02444305,0.001204364,0.003199126,0.00286143,0.000669991],"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":[0.0007488832,0.0002329545,0.02896598,0.0003848239,0.0002061247,0.001024802,0.001081798,0.5225713,0.004589808,0.1508904,0.01526732,0.2740357],"study_design_scores_gemma":[0.00002390046,0.00003095741,0.001160627,0.00005080417,0.00002023047,0.0002110865,0.0003431069,0.8421039,0.001582697,0.1504464,0.004003907,0.00002236504],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.2638543,0.005037724,0.6888657,0.02545037,0.0007358596,0.0002790293,0.001193223,0.001597286,0.01298652],"genre_scores_gemma":[0.8685431,0.001243149,0.1268308,0.0005771682,0.0002397221,0.0000822687,0.0004798423,0.0001077849,0.001896179],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008863359,"threshold_uncertainty_score":0.04361242,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.06819232942788313,"score_gpt":0.2586478427075545,"score_spread":0.1904555132796714,"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."}}