{"id":"W4409576369","doi":"10.61091/jcmcc127a-105","title":"A quantitative analysis study of FinTech on banks’ operational efficiency and risk management based on Monte Carlo simulation","year":2025,"lang":"en","type":"article","venue":"Journal of Combinatorial Mathematics and Combinatorial Computing","topic":"FinTech, Crowdfunding, Digital Finance","field":"Business, Management and Accounting","cited_by":0,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Monte Carlo method; Operational risk management; Operational risk; Computer science; Risk management; Risk analysis (engineering); Reliability engineering; Business; Statistics; Engineering; Mathematics; Finance","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":"metacan-v3-hybrid-931329e0061c","candidate_categories":[],"consensus_categories":[],"category_scores_codex":[0.002497266,0.0004445359,0.0004825957,0.001368228,0.0004703002,0.001275773,0.0006195511,0.0007678238,0.002157459],"category_scores_gemma":[0.01093376,0.0002244511,0.0006298093,0.001490603,0.0007806192,0.0017879,0.000637405,0.0007239366,0.0001310213],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001648815,"about_ca_system_score_gemma":0.001167427,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.007232825,"about_ca_topic_score_gemma":0.004038701,"domain_scores_codex":[0.998583,0.0005874378,0.00007029795,0.0001800319,0.000351163,0.0002281208],"domain_scores_gemma":[0.9859082,0.009907263,0.001863132,0.0006925373,0.001316596,0.0003123179],"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":[0.00008846774,0.0001017852,0.05049473,0.00008559284,0.00009701058,0.0002699549,0.000186101,0.8981474,0.0009907072,0.03255504,0.001027157,0.01595597],"study_design_scores_gemma":[0.00001127816,0.00009136611,0.01726446,0.00002422428,0.00005118268,0.0001002464,0.0001872663,0.968986,0.0008217792,0.01167469,0.0007557143,0.00003172197],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.9163405,0.0006656306,0.06782155,0.0006633562,0.00002783456,0.00007618014,0.0003287655,0.0001604012,0.01391568],"genre_scores_gemma":[0.9962803,0.0001433511,0.002874903,0.00002870578,0.000007781842,0.0000231786,0.00007500047,0.000008046356,0.0005586257],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.007232825,"threshold_uncertainty_score":0.01438141,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.01444152913513973,"score_gpt":0.2710308301304726,"score_spread":0.2565893009953328,"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."}}