{"id":"W4205435749","doi":"10.1108/jmlc-11-2021-0123","title":"Money laundering influence on financial institutions and ways to retaliate","year":2022,"lang":"en","type":"article","venue":"Journal of Money Laundering Control","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Financial institution; Business; Variety (cybernetics); Financial services; Finance; Accounting; Originality; Compliance (psychology); Institution; Value (mathematics); Law; Political science","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":[0.004437608,0.0001657545,0.0002886862,0.002555495,0.00539012,0.006592482,0.0006238733,0.001165532,0.006774033],"category_scores_gemma":[0.02241627,0.0001771298,0.0001244181,0.001973615,0.008777088,0.003093191,0.003393539,0.00144479,0.0002902394],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.004309422,"about_ca_system_score_gemma":0.004049025,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.009907611,"about_ca_topic_score_gemma":0.01431419,"domain_scores_codex":[0.9922243,0.004602675,0.0002878662,0.000450389,0.001248563,0.001186114],"domain_scores_gemma":[0.965067,0.01541553,0.01400619,0.001347272,0.002081684,0.002082209],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"qualitative","study_design_gemma":"observational","study_design_scores_codex":[0.0001493414,0.0003667665,0.360471,0.0004679554,0.00005701478,0.002861148,0.3783284,0.0008839784,0.001228898,0.1039408,0.004863143,0.1463817],"study_design_scores_gemma":[0.00001956468,0.0002194313,0.3612423,0.001443926,0.0000514688,0.001701313,0.5045455,0.001523881,0.001637805,0.01703285,0.1104886,0.00009345233],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9251289,0.001245694,0.0009704373,0.004559714,0.00003061333,0.00003725146,0.00003324678,0.00001843149,0.0679757],"genre_scores_gemma":[0.9983322,0.0002623663,0.0001346218,0.0001491624,0.0000138317,0.000005438103,0.000005717216,0.000002463378,0.001094258],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.009907611,"threshold_uncertainty_score":0.03126717,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.03771557503993421,"score_gpt":0.2895029055303233,"score_spread":0.251787330490389,"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."}}