{"id":"W4386848513","doi":"10.3390/risks11090164","title":"Machine Learning in Forecasting Motor Insurance Claims","year":2023,"lang":"en","type":"article","venue":"Risks","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":22,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Actuarial science; Random forest; Quarter (Canadian coin); Econometrics; Computer science; Economics; Business; Artificial intelligence","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":false,"about_ca":true,"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":[0.002750448,0.0007556745,0.001042331,0.001614662,0.0003358771,0.001067192,0.0007429277,0.001218413,0.0009772098],"category_scores_gemma":[0.006131533,0.000329121,0.0006109643,0.001732275,0.0003506674,0.001121372,0.0004332976,0.001355868,0.0003374852],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0007691071,"about_ca_system_score_gemma":0.0007567652,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01290833,"about_ca_topic_score_gemma":0.006229066,"domain_scores_codex":[0.9993033,0.0002851532,0.00006124776,0.0001379112,0.0001167919,0.00009560056],"domain_scores_gemma":[0.9966889,0.002625165,0.0002145545,0.00009215227,0.0003132715,0.00006586466],"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":[0.00009188176,0.0001078626,0.008559329,0.00008304745,0.00007488836,0.00006225207,0.00003188987,0.9028388,0.0003255913,0.001746133,0.001877274,0.08420099],"study_design_scores_gemma":[0.000003216225,0.00001105789,0.0009576195,0.00001130467,0.000003715263,0.000004638538,0.000007801847,0.9972024,0.0001199842,0.001418263,0.0002562116,0.0000038393],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.5526294,0.01667945,0.4142212,0.004440419,0.0005915431,0.0001783753,0.001894683,0.001497153,0.007867824],"genre_scores_gemma":[0.959569,0.001455383,0.0361807,0.0001991076,0.0002523695,0.00005952658,0.0009126947,0.00003002144,0.00134118],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01290833,"threshold_uncertainty_score":0.02566642,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.09004306024869731,"score_gpt":0.2568708583974556,"score_spread":0.1668277981487583,"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."}}