{"id":"W4321327316","doi":"10.1002/asmb.2750","title":"Micro‐level reserving for general insurance claims using a long short‐term memory network","year":2023,"lang":"en","type":"article","venue":"Applied Stochastic Models in Business and Industry","topic":"Probability and Risk Models","field":"Decision Sciences","cited_by":6,"is_retracted":false,"has_abstract":true,"ca_institutions":"Université Laval","funders":"Natural Sciences and Engineering Research Council of Canada","keywords":"Computer science; Aggregate (composite); Payment; Term (time); Predictive power; Artificial neural network; Econometrics; Estimation; Scheme (mathematics); Data mining; Machine learning; Artificial intelligence; Economics; Mathematics","routes":{"ca_aff":true,"ca_fund":true,"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":[0.0007242327,0.0004188049,0.000415353,0.0007150266,0.000217374,0.0004927395,0.0007854241,0.000602566,0.001287828],"category_scores_gemma":[0.001496613,0.000197321,0.0003583898,0.0005502362,0.0003110559,0.0009881741,0.0004747943,0.0009112183,0.0002238949],"about_ca_system_candidate":false,"about_ca_system_consensus":false,"about_ca_system_score_codex":0.0009041831,"about_ca_system_score_gemma":0.0004680915,"about_ca_topic_candidate":false,"about_ca_topic_consensus":false,"about_ca_topic_score_codex":0.01042917,"about_ca_topic_score_gemma":0.0131602,"domain_scores_codex":[0.9998684,0.00002354181,0.00000878899,0.00004791462,0.00002337828,0.00002796325],"domain_scores_gemma":[0.9995394,0.0002427622,0.00007915999,0.0000429862,0.00006432323,0.00003147596],"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":[0.0002885168,0.0001401896,0.01094153,0.00003159394,0.00005050045,0.0001281169,0.00005670191,0.9052356,0.001681628,0.002058385,0.001183195,0.07820404],"study_design_scores_gemma":[0.00000165385,0.000007392057,0.0004519633,0.000001482126,0.00000226061,0.000003561842,0.000002684643,0.9986907,0.0001767823,0.0006291094,0.00003101917,0.000001495947],"study_design_candidate":"simulation_or_modeling","study_design_consensus":"simulation_or_modeling","genre_codex":"empirical","genre_gemma":"empirical","genre_scores_codex":[0.8216325,0.0005949808,0.1728135,0.0008557867,0.00005699341,0.00003325556,0.0006828434,0.0009571495,0.00237303],"genre_scores_gemma":[0.9881517,0.00008243797,0.01036961,0.00004328366,0.00001457916,0.00001794569,0.000319128,0.000008635079,0.0009925012],"genre_candidate":"empirical","genre_consensus":"empirical","teacher_disagreement_score":0.01042917,"threshold_uncertainty_score":0.02073693,"prediction_status":"machine_predicted_unvalidated"},"machine_scores":{"provisional":true,"baseline":true,"maturity_gate_passed":false,"score_opus":0.2790555256484452,"score_gpt":0.3770115476524035,"score_spread":0.09795602200395825,"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."}}