{"id":"W4410479764","doi":"10.9734/ajarr/2025/v19i51017","title":"RegTech and Blockchain Integration in AML Compliance: Financial and Operational Impacts","year":2025,"lang":"en","type":"article","venue":"Asian Journal of Advanced Research and Reports","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":4,"is_retracted":false,"has_abstract":true,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Blockchain; Compliance (psychology); Business; Computer science; Computer security; Psychology","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":[0.03082976,0.0005019217,0.0005300693,0.003796846,0.001584812,0.00912537,0.001074537,0.002116748,0.007083616],"category_scores_gemma":[0.05710743,0.0002990972,0.0008340759,0.005641473,0.003587579,0.008363486,0.006090143,0.001545667,0.000621887],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.005787412,"about_ca_system_score_gemma":0.01308549,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003177607,"about_ca_topic_score_gemma":0.005031774,"domain_scores_codex":[0.9566013,0.02635619,0.002403084,0.00195996,0.010488,0.002191538],"domain_scores_gemma":[0.8659186,0.09423025,0.02235602,0.003510174,0.01223799,0.001746893],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"observational","study_design_scores_codex":[0.001057142,0.0006860663,0.147796,0.02188013,0.0009590799,0.002522905,0.01899707,0.0133426,0.004456824,0.1744177,0.006138793,0.6077456],"study_design_scores_gemma":[0.0004413594,0.004158194,0.2414672,0.07621992,0.005226194,0.004947693,0.09485798,0.02603864,0.03084074,0.126841,0.3883863,0.0005747852],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.7108399,0.06568185,0.02260588,0.02395734,0.0004670846,0.001173862,0.0008950955,0.0002657832,0.1741132],"genre_scores_gemma":[0.9862429,0.007153598,0.003958562,0.0006419437,0.00006684073,0.0001175709,0.0001255558,0.00001971865,0.001673245],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.03082976,"threshold_uncertainty_score":0.1630453,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.02987502594506936,"score_gpt":0.3285626918051449,"score_spread":0.2986876658600756,"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."}}