{"id":"W4409149437","doi":"10.1016/j.jfds.2025.100163","title":"Finding a needle in a haystack: A machine learning framework for anomaly detection in payment systems","year":2025,"lang":"en","type":"article","venue":"The Journal of Finance and Data Science","topic":"Anomaly Detection Techniques and Applications","field":"Computer Science","cited_by":13,"is_retracted":false,"has_abstract":true,"ca_institutions":"Bank of Canada","funders":"","keywords":"Haystack; Payment; Anomaly detection; Computer science; Anomaly (physics); Artificial intelligence; Machine learning; World Wide Web","routes":{"ca_aff":true,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.005523994,0.001261101,0.001653149,0.002914597,0.00103104,0.002789469,0.00330261,0.002279737,0.001063025],"category_scores_gemma":[0.01429675,0.0007312375,0.001843881,0.00196925,0.002754702,0.003796426,0.002939306,0.003995632,0.0004317949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00149539,"about_ca_system_score_gemma":0.001860606,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.008428644,"about_ca_topic_score_gemma":0.005487401,"domain_scores_codex":[0.9964389,0.001497434,0.000241471,0.0007444875,0.0007856538,0.0002920794],"domain_scores_gemma":[0.9899853,0.006550127,0.001081212,0.0008212593,0.00122199,0.0003401311],"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":[0.000100967,0.0002123141,0.008010762,0.0001269736,0.0001998294,0.0002187589,0.000344657,0.8094454,0.002375512,0.05466386,0.001479719,0.1228212],"study_design_scores_gemma":[0.000003533435,0.00001708981,0.000202495,0.000006479938,0.000005869353,0.00001518707,0.00001015996,0.9821846,0.0002195831,0.01706262,0.0002643073,0.000008040915],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.004636426,0.0001471831,0.9943592,0.0003191954,0.00001456129,0.00002393189,0.0000317165,0.0002928772,0.0001748459],"genre_scores_gemma":[0.3724352,0.0003551586,0.6249154,0.0003472464,0.0002241633,0.0001693797,0.0002455314,0.0001157268,0.00119231],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.008428644,"threshold_uncertainty_score":0.02921402,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02964399627286791,"score_gpt":0.3219928109928626,"score_spread":0.2923488147199947,"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."}}