{"id":"W4408800633","doi":"10.69554/tdhs1278","title":"Social media as a compliance risk for financial services: Exploring emerging risks and finding solutions to mitigate harm","year":2025,"lang":"en","type":"article","venue":"Journal of financial compliance.","topic":"Cybercrime and Law Enforcement Studies","field":"Computer Science","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"Global Relay (Canada)","funders":"","keywords":"Harm; Compliance (psychology); Business; Social media; Financial risk; Financial services; Risk analysis (engineering); Actuarial science; Finance; Psychology; Political science; Social psychology; Law","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":"codex-gemma-dda1882f352a","candidate_categories":["metaepi_narrow","sts"],"consensus_categories":[],"category_scores_codex":[0.0007052962,0.0002641722,0.0005703261,0.0002969264,0.001607887,0.000189021,0.000805332,0.00007945653,0.00000502798],"category_scores_gemma":[0.0004876535,0.0002672758,0.0002350909,0.000686488,0.0000794131,0.0007487219,0.0004723291,0.0003766492,0.00001245188],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0001337281,"about_ca_system_score_gemma":0.0002533747,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.00009749104,"about_ca_topic_score_gemma":0.0003109128,"domain_scores_codex":[0.9978755,0.00005304477,0.0007572944,0.0003652286,0.0003440411,0.0006049157],"domain_scores_gemma":[0.9984206,0.0003193186,0.0004586117,0.0002127419,0.00044322,0.0001454935],"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":[0.0008187451,0.0003621956,0.01135424,0.001278686,0.000318799,0.00009348511,0.03504776,0.001386926,0.001590356,0.4305455,0.03333359,0.4838697],"study_design_scores_gemma":[0.005791287,0.0007546523,0.6263735,0.004848334,0.0002855161,0.0000575838,0.001248363,0.005552784,0.001901074,0.1145875,0.2372022,0.001397212],"study_design_candidate":"observational","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.4344815,0.002250955,0.5519151,0.004762339,0.004122008,0.0006902268,0.00006410947,0.00009838031,0.001615337],"genre_scores_gemma":[0.9803019,0.0005047366,0.01665254,0.001278137,0.001026225,0.00008807062,0.000002109937,0.00001599537,0.0001302606],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.6150193,"threshold_uncertainty_score":0.9999779,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.195802359529271,"score_gpt":0.3623492217605147,"score_spread":0.1665468622312437,"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."}}