{"id":"W2562377713","doi":"10.1109/uemcon.2016.7777919","title":"A new algorithm for money laundering detection based on structural similarity","year":2016,"lang":"en","type":"article","venue":"","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":37,"is_retracted":false,"has_abstract":true,"ca_institutions":"York University","funders":"","keywords":"Money laundering; Computer science; Task (project management); Process (computing); Cluster analysis; Set (abstract data type); Similarity (geometry); Data mining; Data set; Financial transaction; Reduction (mathematics); Algorithm; Finance; Business; Machine learning; Artificial intelligence; Database; Database transaction; Image (mathematics); Economics; Mathematics","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":[0.001242454,0.001087941,0.002103438,0.006609323,0.001525095,0.001700508,0.003441591,0.001783394,0.002776905],"category_scores_gemma":[0.005187551,0.0007578333,0.00145818,0.004312028,0.001030958,0.002410129,0.002084045,0.001793404,0.001834991],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009433572,"about_ca_system_score_gemma":0.002090036,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.006539494,"about_ca_topic_score_gemma":0.007573179,"domain_scores_codex":[0.9975508,0.000258264,0.0002372835,0.0007805399,0.0009904467,0.0001827229],"domain_scores_gemma":[0.9976623,0.0006183402,0.0002794655,0.0003653461,0.0009484087,0.0001261996],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0001879191,0.0004233373,0.00492566,0.000204963,0.0001731332,0.0001804488,0.0003516169,0.02796984,0.02088211,0.007090475,0.00893079,0.9286797],"study_design_scores_gemma":[0.00009230889,0.0002046264,0.004530307,0.00003807034,0.00008965872,0.0007881572,0.000300025,0.9512347,0.01492649,0.0134209,0.01430147,0.00007328564],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.0121539,0.0002379153,0.9841049,0.0002161611,0.00008511935,0.0002929444,0.000175903,0.001699964,0.001033275],"genre_scores_gemma":[0.05709253,0.0001166819,0.9396622,0.0001108731,0.00006319527,0.0002870223,0.0007105196,0.0001381368,0.001818848],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.006609323,"threshold_uncertainty_score":0.01300287,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02745587934902836,"score_gpt":0.2978497972329219,"score_spread":0.2703939178838936,"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."}}