{"id":"W4388439677","doi":"10.7202/1091945ar","title":"The Pricing of Multiple Line P&amp;C Insurance Based on the Full Information Underwriting Beta","year":2009,"lang":"en","type":"article","venue":"Assurances et gestion des risques","topic":"Insurance and Financial Risk Management","field":"Economics, Econometrics and Finance","cited_by":2,"is_retracted":false,"has_abstract":true,"ca_institutions":"","funders":"","keywords":"Underwriting; Auto insurance risk selection; Actuarial science; General insurance; Property insurance; Business; Liability insurance; Line of business; Insurance policy; Casualty insurance; Bond insurance; Key person insurance; Business model; Marketing","routes":{"ca_aff":false,"ca_fund":false,"ca_venue":true,"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":[0.001689813,0.0008895564,0.001029587,0.0009889973,0.0004310967,0.001984556,0.002008165,0.001548567,0.002506168],"category_scores_gemma":[0.006113123,0.0006633562,0.001273588,0.0006162652,0.001414812,0.003489814,0.0008211985,0.00188234,0.0003993328],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.001935335,"about_ca_system_score_gemma":0.001149451,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01304513,"about_ca_topic_score_gemma":0.005207273,"domain_scores_codex":[0.9992868,0.0002078226,0.0000211542,0.0001254416,0.0001823157,0.0001764991],"domain_scores_gemma":[0.9981439,0.0009683604,0.0004026261,0.0001457438,0.0001509634,0.0001882879],"domain_codex":null,"domain_gemma":null,"domain_candidate":null,"domain_consensus":null,"study_design_codex":"simulation_or_modeling","study_design_gemma":"not_applicable","study_design_scores_codex":[0.0001077609,0.00009071762,0.004692314,0.00004484112,0.00006525272,0.0005864428,0.0001726569,0.8287268,0.002425791,0.1473369,0.001345982,0.01440458],"study_design_scores_gemma":[0.00001008553,0.00001904464,0.0009611394,0.000004409635,0.000009729675,0.00008404187,0.000009249108,0.9813066,0.0001024704,0.01728201,0.0001953035,0.00001593766],"study_design_candidate":"not_applicable","study_design_consensus":null,"genre_codex":"methods","genre_gemma":"empirical","genre_scores_codex":[0.3391319,0.0009445472,0.6423942,0.00148055,0.00006999032,0.00007153068,0.0002905412,0.0003475208,0.01526933],"genre_scores_gemma":[0.9829246,0.0003450383,0.01242416,0.00005465771,0.00006859792,0.00002944684,0.00006827843,0.00003031164,0.0040549],"genre_candidate":"empirical","genre_consensus":null,"teacher_disagreement_score":0.01304513,"threshold_uncertainty_score":0.02593839,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.04386115906498722,"score_gpt":0.2468156142094343,"score_spread":0.202954455144447,"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."}}