{"meta":{"query_hash":"63d69f8870a9","filters":{"venue":"American Journal of Financial Technology and Innovation"},"cohort_total":1,"direct_labels_cover":0,"predictions_cover":1,"exported":1,"export_cap":100000,"truncated":false,"label_status":"direct model label, unvalidated","prediction_status":"machine_predicted_unvalidated (Codex and Gemma teacher distillation)","score_status":"score_only:v0-immature-baseline","snapshot":{"source":"OpenAlex, pinned release, all 482 partitions","release":"2026-06-24","frame_built":"2026-07-12"},"permalink":"https://metacan.xera.ac/q/63d69f8870a9","api":"https://metacan.xera.ac/api/v1/cohort?venue=American+Journal+of+Financial+Technology+and+Innovation"},"results":[{"id":"W4415543961","doi":"10.54536/ajfti.v3i1.5168","title":"AI-Driven Fraud Detection in Digital Banking: Ml Approach for Secure and Transparent Financial Transactions","year":2025,"lang":"","type":"article","venue":"American Journal of Financial Technology and Innovation","topic":"Blockchain Technology Applications and Security","field":"Computer Science","cited_by":1,"is_retracted":false,"has_abstract":true,"route_ca_aff":true,"route_ca_fund":false,"route_ca_venue":false,"route_about_ca":false,"ca_institutions":"Nexen (Canada)","funders":"","keywords":"Financial services; General partnership; Financial transaction; Data breach; FinTech; Financial sector; Focus (optics); Digital transformation","score_opus":0.008748095268767214,"score_gpt":0.24587979946533386,"score_spread":0.23713170419656665,"validation_status":"score_only:v0-immature-baseline","prediction":{"id":"W4415543961","genre_codex":"methods","genre_gemma":"empirical","domain_codex":null,"domain_gemma":null,"model_version":"metacan-v3-hybrid-931329e0061c","genre_candidate":"empirical","genre_consensus":null,"domain_candidate":null,"domain_consensus":null,"prediction_status":"machine_predicted_unvalidated","genre_scores_codex":[0.034484588,0.0008699587,0.95579875,0.0025651208,0.00008368199,0.000095962365,0.00007162643,0.0006168125,0.005413452],"genre_scores_gemma":[0.83497643,0.000572736,0.15893392,0.0005057835,0.000208696,0.00010089675,0.0001131939,0.0000662492,0.004522112],"study_design_codex":"simulation_or_modeling","study_design_gemma":"simulation_or_modeling","domain_scores_codex":[0.99826247,0.00073328114,0.000108135195,0.0002522762,0.0004584349,0.00018545952],"domain_scores_gemma":[0.99476296,0.0032275422,0.0005005522,0.00047843147,0.0007979502,0.00023262826],"candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.0026789315,0.00048438943,0.0009724967,0.0018050675,0.00082341715,0.0032148105,0.0016675014,0.0015981703,0.0019154114],"category_scores_gemma":[0.008646442,0.00040637972,0.0007401797,0.0013481189,0.0014120908,0.0032472657,0.0020898231,0.0023636029,0.0005215453],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_system_candidate":false,"about_ca_system_consensus":false,"study_design_scores_codex":[0.00026087396,0.00033595975,0.007502097,0.00024626596,0.00020226448,0.00060014345,0.0008223098,0.5212196,0.005702303,0.13199915,0.0041285213,0.3269805],"study_design_scores_gemma":[0.0000047945437,0.00001591415,0.00023983479,0.000010828914,0.000008701624,0.000046764646,0.000034021807,0.97549325,0.00061938143,0.02264676,0.00087264733,0.0000070968936],"about_ca_topic_score_codex":0.002844208,"about_ca_topic_score_gemma":0.0017776656,"teacher_disagreement_score":0.0032148105,"about_ca_system_score_codex":0.0013322174,"about_ca_system_score_gemma":0.0016022704,"threshold_uncertainty_score":0.014167726},"labels":[],"label_agreement":null}]}