{"id":"W4384209454","doi":"10.1002/ail2.85","title":"Predicting mobile money transaction fraud using machine learning algorithms","year":2023,"lang":"en","type":"article","venue":"Applied AI Letters","topic":"Crime, Illicit Activities, and Governance","field":"Social Sciences","cited_by":26,"is_retracted":false,"has_abstract":true,"ca_institutions":"Royal Roads University","funders":"","keywords":"Money laundering; Database transaction; Random forest; Mobile payment; Financial transaction; Computer science; Logistic regression; Law enforcement; Classifier (UML); Transaction data; Artificial intelligence; Machine learning; Algorithm; Payment; 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":[0.002211248,0.00066974,0.000581974,0.00356036,0.0004119655,0.001399119,0.0006135561,0.001027228,0.001236288],"category_scores_gemma":[0.007282215,0.0001555242,0.0004951297,0.001683896,0.00027618,0.001028382,0.000387608,0.0008302503,0.0005592974],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0008576167,"about_ca_system_score_gemma":0.0008417103,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008164813,"about_ca_topic_score_gemma":0.004511024,"domain_scores_codex":[0.9991249,0.0003881098,0.00006766298,0.0001288335,0.0001558509,0.0001345338],"domain_scores_gemma":[0.9951402,0.003110729,0.0006307209,0.0001893865,0.0007735246,0.0001554759],"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":[0.0003263841,0.0009357903,0.2111696,0.00006924268,0.0001633379,0.000247711,0.00008082206,0.6183537,0.001079877,0.00159687,0.004117211,0.1618592],"study_design_scores_gemma":[0.000002907842,0.00002612891,0.004906381,0.000009417106,0.00000419955,0.00001453616,0.00003623691,0.9941987,0.0002214038,0.0004330176,0.0001431542,0.000003827477],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9396863,0.0005638192,0.05501271,0.0007677786,0.00009226418,0.0001289909,0.0006945433,0.0004700084,0.002583596],"genre_scores_gemma":[0.9855795,0.00008908333,0.01311147,0.0000339852,0.0000310729,0.00002985694,0.0005239241,0.000005704778,0.0005954799],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.008164813,"threshold_uncertainty_score":0.01623458,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02060281957099611,"score_gpt":0.2807653426111022,"score_spread":0.2601625230401061,"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."}}