{"id":"W4293795496","doi":"10.1080/19361610.2022.2114744","title":"Predicting Money Laundering Using Machine Learning and Artificial Neural Networks Algorithms in Banks","year":2022,"lang":"en","type":"article","venue":"Journal of Applied Security Research","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":54,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Artificial neural network; Computer science; Random forest; Machine learning; Naive Bayes classifier; Artificial intelligence; Algorithm; Finance; Business; Support vector machine","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":"codex-gemma-dda1882f352a","candidate_categories":["sts","research_integrity"],"consensus_categories":[],"category_scores_codex":[0.00659745,0.0001003106,0.0002627948,0.000283809,0.001693141,0.0001950087,0.0002889672,0.00007700842,0.00008562641],"category_scores_gemma":[0.0002590163,0.0001098335,0.00004877135,0.0006941704,0.0002534615,0.0002453515,0.0003269187,0.002861698,2.411312e-7],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0004590256,"about_ca_system_score_gemma":0.0002164691,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00571736,"about_ca_topic_score_gemma":0.002921786,"domain_scores_codex":[0.9967896,0.0005774319,0.0004030976,0.000202609,0.001426185,0.0006011229],"domain_scores_gemma":[0.9988356,0.0005715652,0.0002501002,0.00007150292,0.0001112062,0.000159995],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"simulation_or_modeling","study_design_scores_codex":[0.002896512,0.001027744,0.1042923,0.0001482874,0.000167178,0.0007457689,0.533314,0.2314218,0.01194062,0.01893366,0.0003199057,0.09479222],"study_design_scores_gemma":[0.000934945,0.0003693551,0.001374313,0.00005041414,0.00001671453,0.00006477446,0.1235425,0.8563744,0.0002300192,0.008351305,0.008379319,0.0003119821],"study_design_candidate":"simulation_or_modeling","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9959204,0.0009476769,0.0001366038,0.0002601333,0.0002343025,0.0001858328,0.000004066746,0.00001364362,0.002297398],"genre_scores_gemma":[0.9987497,0.0002692294,0.0001204023,0.00002377868,0.0007711911,0.000006124696,9.773436e-7,0.00001711291,0.00004149781],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.6249526,"threshold_uncertainty_score":0.9996065,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.07718883552287828,"score_gpt":0.3758919006955296,"score_spread":0.2987030651726513,"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."}}