{"id":"W4415722142","doi":"10.3390/jrfm18110612","title":"A Systematic Review of Artificial Intelligence Applied to Compliance: Fraud Detection in Cryptocurrency Transactions","year":2025,"lang":"en","type":"article","venue":"Journal of risk and financial management","topic":"Imbalanced Data Classification Techniques","field":"Computer Science","cited_by":5,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"Agencia Estatal de Investigación; European Commission; Ministerio de Ciencia, Innovación y Universidades","keywords":"Cryptocurrency; Transparency (behavior); Bridging (networking); Key (lock); Quality (philosophy); Applications of artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"about_ca":false,"invisible_to_affiliation_only":true},"retraction":null,"screen":null,"direct_labels":[],"prediction":{"model_version":"codex-gemma-dda1882f352a","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0007863799,0.00007818665,0.0003363426,0.0003975017,0.00004963643,0.0000279706,0.0003868061,0.00002806462,0.000001798246],"category_scores_gemma":[0.0001133359,0.0000695143,0.00005238152,0.000925377,0.00001967565,0.0001404628,0.00004228578,0.000144307,0.000002228842],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.00005521235,"about_ca_system_score_gemma":0.00002708153,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.000004534097,"about_ca_topic_score_gemma":0.00001025478,"domain_scores_codex":[0.9987266,0.00005442221,0.0008119025,0.0001360127,0.0001783958,0.00009264045],"domain_scores_gemma":[0.9991878,0.00005120728,0.0003835393,0.0002422676,0.0001067707,0.00002841214],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"systematic_review","study_design_scores_codex":[0.0000232655,0.0001254013,0.00001084768,0.04848899,0.00001285274,0.00000371981,0.0002485805,0.00004282719,0.0000956682,0.07430001,0.00007643487,0.8765714],"study_design_scores_gemma":[0.0009683651,0.0009845343,0.02492532,0.6003807,0.0009313355,0.00004017544,0.00102089,0.013576,0.0261723,0.3236348,0.00619328,0.001172329],"study_design_candidate":"design_other","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.000174408,0.001960775,0.9967938,0.0001382606,0.0001816957,0.0005893499,0.000003658297,0.00001300286,0.0001450136],"genre_scores_gemma":[0.8666486,0.02181883,0.110945,0.0004089928,0.00002411503,0.0001334798,5.652784e-7,0.000004578261,0.00001591875],"genre_candidate":"methods","genre_consensus":null,"teacher_disagreement_score":0.8858489,"threshold_uncertainty_score":0.2834711,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01751035328781938,"score_gpt":0.2810347906765492,"score_spread":0.2635244373887298,"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."}}