{"id":"W4306147494","doi":"10.21203/rs.3.rs-2161095/v1","title":"Predicting Money Laundering using Machine Learning and Artificial Neural Networks Algorithms in Banks","year":2022,"lang":"en","type":"preprint","venue":"Research Square","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":7,"is_retracted":false,"has_abstract":false,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Artificial neural network; Computer science; Random forest; Machine learning; Artificial intelligence; Naive Bayes classifier; Algorithm; Financial transaction; Finance; Business; Database; Support vector machine; Database transaction","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":[0.002466029,0.0005405412,0.0006870319,0.001257875,0.0006758873,0.001557625,0.0007848544,0.001444448,0.001978871],"category_scores_gemma":[0.009991473,0.0003853929,0.0004333776,0.001045371,0.0005501518,0.001362852,0.0006311836,0.001395383,0.0004437264],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001813032,"about_ca_system_score_gemma":0.001308308,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.05270024,"about_ca_topic_score_gemma":0.04604021,"domain_scores_codex":[0.9994525,0.0002420263,0.00004421892,0.0001126013,0.00005132112,0.00009732667],"domain_scores_gemma":[0.9921299,0.005422328,0.0008263246,0.0001906189,0.001010207,0.0004206881],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"observational","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.0007877713,0.0009162257,0.5685304,0.00007663687,0.000130284,0.0003300011,0.0002871646,0.3967066,0.0004787996,0.002921457,0.003865314,0.02496942],"study_design_scores_gemma":[0.00001443277,0.00002786929,0.03117693,0.00001092158,0.0000115985,0.00001274281,0.0001587409,0.9668315,0.0002477317,0.001388753,0.0001117109,0.000007046869],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9955636,0.0001565036,0.002856103,0.0004980272,0.00001677045,0.00001648393,0.0002706033,0.00003243738,0.0005894565],"genre_scores_gemma":[0.9973411,0.00009976891,0.001414256,0.0000199601,0.00001960097,0.0000143851,0.0003652399,0.000006526996,0.0007192015],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.05270024,"threshold_uncertainty_score":0.1047869,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.1229947721594419,"score_gpt":0.4248837005186906,"score_spread":0.3018889283592487,"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."}}