{"id":"W4366089084","doi":"10.22541/au.168172408.81196220/v1","title":"Predicting Mobile Money Transaction Fraud using Machine Learning Algorithms","year":2023,"lang":"en","type":"preprint","venue":"","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Database transaction; Random forest; Mobile payment; Financial transaction; Logistic regression; Computer science; Classifier (UML); Transaction data; Law enforcement; Artificial intelligence; Machine learning; Payment; Algorithm; Business; Finance; Database; 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":[0.002955619,0.0008151594,0.0007411026,0.003554836,0.0005265165,0.001786879,0.0007683687,0.001399956,0.001173502],"category_scores_gemma":[0.009791561,0.0002246823,0.0006153191,0.001926387,0.0004023379,0.001497999,0.0006137087,0.001188718,0.0006785564],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001018601,"about_ca_system_score_gemma":0.0008832596,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007275761,"about_ca_topic_score_gemma":0.004248485,"domain_scores_codex":[0.9988117,0.0005223734,0.00009295822,0.0001762137,0.0002131601,0.0001836064],"domain_scores_gemma":[0.9940619,0.003911157,0.0007112194,0.0003137169,0.0007936307,0.0002083354],"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":[0.0003154332,0.0008239057,0.1775252,0.0000839261,0.0001925673,0.0003082874,0.0001262294,0.6231556,0.0009306776,0.002866226,0.005375508,0.1882964],"study_design_scores_gemma":[0.000003899299,0.00002522211,0.004609072,0.00001209626,0.000004862744,0.00001964218,0.00004775556,0.993593,0.0002613777,0.00115731,0.0002604024,0.000005258442],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"methods","genre_scores_codex":[0.9136513,0.0008874301,0.07958932,0.00134437,0.0001269353,0.0001395596,0.0007627186,0.0005868445,0.002911645],"genre_scores_gemma":[0.975306,0.000180167,0.02249328,0.00005641613,0.00006011437,0.00004221112,0.0008417635,0.00001293544,0.001007107],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.007275761,"threshold_uncertainty_score":0.01563096,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.0631283786499257,"score_gpt":0.3348007764479293,"score_spread":0.2716723977980036,"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."}}