{"id":"W3130250864","doi":"10.2139/ssrn.3723780","title":"Machine Learning in Property and Casualty Insurance: A Review for Pricing and Reserving","year":2020,"lang":"en","type":"review","venue":"SSRN Electronic Journal","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":8,"is_retracted":false,"has_abstract":false,"ca_institutions":"Université Laval","funders":"","keywords":"Property insurance; Actuarial science; Business; Property (philosophy); Automobile insurance; Casualty insurance; Insurance policy","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":[0.001902253,0.000878314,0.002158034,0.00262963,0.0002404936,0.0018858,0.001086149,0.00184139,0.004769146],"category_scores_gemma":[0.004671732,0.0003701947,0.0009819949,0.004920589,0.000794677,0.002496149,0.0008862211,0.002204359,0.001626278],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001040002,"about_ca_system_score_gemma":0.00228877,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.003298062,"about_ca_topic_score_gemma":0.004126505,"domain_scores_codex":[0.9995168,0.0001244925,0.00007669527,0.0001067301,0.0001467787,0.00002839295],"domain_scores_gemma":[0.9966577,0.002372585,0.0002777294,0.00007651527,0.0005212743,0.00009414986],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"design_other","study_design_gemma":"not_applicable","study_design_scores_codex":[0.00007431248,0.00007650444,0.0002910497,0.01679156,0.0001734014,0.00003744775,0.0000246511,0.001189121,0.0002715245,0.006528537,0.03910511,0.9354367],"study_design_scores_gemma":[0.00009066003,0.0003008905,0.003191171,0.02296205,0.0006872138,0.000483614,0.00008241924,0.00235746,0.0004761976,0.01747462,0.951806,0.00008771056],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"review","genre_gemma":"review","genre_scores_codex":[0.00005003293,0.9990001,0.000234223,0.0003590676,0.0001176886,0.00000228274,0.00001931209,0.000003974006,0.0002133659],"genre_scores_gemma":[0.0007955004,0.9978264,0.0004350811,0.0002902551,0.0004030713,0.000004729136,0.00003271136,0.000003209288,0.0002090202],"genre_candidate":"review","genre_consensus":"review","teacher_disagreement_score":0.004769146,"threshold_uncertainty_score":0.01595438,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04129511454048038,"score_gpt":0.2764173607193512,"score_spread":0.2351222461788709,"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."}}