{"id":"W4321105142","doi":"10.1080/13504851.2023.2176435","title":"Predicting money laundering sanctions using machine learning algorithms and artificial neural networks","year":2023,"lang":"en","type":"article","venue":"Applied Economics Letters","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":9,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Sanctions; Money laundering; Artificial neural network; Transparency (behavior); Machine learning; Artificial intelligence; Support vector machine; Logistic regression; Algorithm; Computer science; Financial crisis; Economics; Finance; Law; Computer security; Political science; Macroeconomics","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":[0.003761307,0.0008605538,0.0006419962,0.003139918,0.0004392074,0.001745907,0.0005782471,0.001053363,0.0008461714],"category_scores_gemma":[0.01484763,0.0002914173,0.0004244843,0.001934094,0.0004336933,0.001368465,0.0006781307,0.001232012,0.0002274299],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001489751,"about_ca_system_score_gemma":0.0009936269,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01103674,"about_ca_topic_score_gemma":0.01007719,"domain_scores_codex":[0.9986094,0.0007203975,0.0001327858,0.0001561165,0.0002325295,0.0001488531],"domain_scores_gemma":[0.9871677,0.009531735,0.00175999,0.0002612927,0.001038773,0.0002405907],"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":[0.0001656316,0.0003162415,0.08617483,0.00006272254,0.0001165036,0.00009928639,0.0000660049,0.8692432,0.0002722392,0.001914134,0.0008622453,0.04070697],"study_design_scores_gemma":[0.000005155822,0.00002907024,0.005495501,0.00001161592,0.00000476656,0.00000859295,0.00002596158,0.9932846,0.0002053706,0.0008369855,0.00008615676,0.000006209981],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9443824,0.0005960346,0.04972392,0.0008366044,0.00007018789,0.0001036669,0.0003917,0.0002220254,0.003673404],"genre_scores_gemma":[0.9851163,0.0001791772,0.01350212,0.00005148117,0.00004721048,0.00004480246,0.0003962122,0.000006620392,0.0006561315],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01103674,"threshold_uncertainty_score":0.021945,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03480438124735694,"score_gpt":0.2534119171452875,"score_spread":0.2186075358979306,"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."}}