{"id":"https://openalex.org/W7163545846","doi":"https://doi.org/10.1016/j.eswa.2026.132994","title":"An explainable hybrid adaptive neuro-fuzzy inference system and deep learning framework for stochastic claims reserving","display_name":"An explainable hybrid adaptive neuro-fuzzy inference system and deep learning framework for stochastic claims reserving","publication_year":2026,"publication_date":"2026-06-04","ids":{"openalex":"https://openalex.org/W7163545846","doi":"https://doi.org/10.1016/j.eswa.2026.132994"},"language":"en","primary_location":{"id":"doi:10.1016/j.eswa.2026.132994","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.eswa.2026.132994","pdf_url":null,"source":{"id":"https://openalex.org/S13144211","display_name":"Expert Systems with Applications","issn_l":"0957-4174","issn":["0957-4174","1873-6793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems with Applications","raw_type":"journal-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":true,"oa_status":"hybrid","oa_url":"https://doi.org/10.1016/j.eswa.2026.132994","any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5062846555","display_name":"Arne Johannssen","orcid":"https://orcid.org/0000-0002-4265-3996"},"institutions":[{"id":"https://openalex.org/I94575722","display_name":"Harz University of Applied Sciences","ror":"https://ror.org/048yn7628","country_code":"DE","type":"education","lineage":["https://openalex.org/I94575722"]}],"countries":["DE"],"is_corresponding":true,"raw_author_name":"Arne Johannssen","raw_affiliation_strings":["Harz University of Applied Sciences, 38855 Wernigerode, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4265-3996","affiliations":[{"raw_affiliation_string":"Harz University of Applied Sciences, 38855 Wernigerode, Germany","institution_ids":["https://openalex.org/I94575722"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5137833178","display_name":"Ali Yeganeh","orcid":"https://orcid.org/0000-0002-1569-9809"},"institutions":[{"id":"https://openalex.org/I159176309","display_name":"Universit\u00e4t Hamburg","ror":"https://ror.org/00g30e956","country_code":"DE","type":"education","lineage":["https://openalex.org/I159176309"]},{"id":"https://openalex.org/I70451448","display_name":"HAW Hamburg","ror":"https://ror.org/00fkqwx76","country_code":"DE","type":"education","lineage":["https://openalex.org/I70451448"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Ali Yeganeh","raw_affiliation_strings":["University of Hamburg 20146 Hamburg, Germany"],"raw_orcid":"https://orcid.org/0000-0002-1569-9809","affiliations":[{"raw_affiliation_string":"University of Hamburg 20146 Hamburg, Germany","institution_ids":["https://openalex.org/I159176309","https://openalex.org/I70451448"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5055024838","display_name":"Nataliya Chukhrova","orcid":"https://orcid.org/0000-0002-4105-7033"},"institutions":[{"id":"https://openalex.org/I223822909","display_name":"Heidelberg University","ror":"https://ror.org/038t36y30","country_code":"DE","type":"education","lineage":["https://openalex.org/I223822909"]}],"countries":["DE"],"is_corresponding":false,"raw_author_name":"Nataliya Chukhrova","raw_affiliation_strings":["University of Heidelberg, 68167 Mannheim, Germany"],"raw_orcid":"https://orcid.org/0000-0002-4105-7033","affiliations":[{"raw_affiliation_string":"University of Heidelberg, 68167 Mannheim, Germany","institution_ids":["https://openalex.org/I223822909"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5062846555"],"corresponding_institution_ids":["https://openalex.org/I94575722"],"apc_list":{"value":3220,"currency":"USD","value_usd":3220},"apc_paid":{"value":3220,"currency":"USD","value_usd":3220},"fwci":0.0,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":{"value":0.73047965,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":null,"biblio":{"volume":"331","issue":null,"first_page":"132994","last_page":"132994"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.210999995470047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T11720","display_name":"Probability and Risk Models","score":0.210999995470047,"subfield":{"id":"https://openalex.org/subfields/1803","display_name":"Management Science and Operations Research"},"field":{"id":"https://openalex.org/fields/18","display_name":"Decision Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.19789999723434448,"subfield":{"id":"https://openalex.org/subfields/1402","display_name":"Accounting"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T12384","display_name":"Customer churn and segmentation","score":0.05790000036358833,"subfield":{"id":"https://openalex.org/subfields/1406","display_name":"Marketing"},"field":{"id":"https://openalex.org/fields/14","display_name":"Business, Management and Accounting"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.7545999884605408},{"id":"https://openalex.org/keywords/inference","display_name":"Inference","score":0.6496999859809875},{"id":"https://openalex.org/keywords/deep-neural-networks","display_name":"Deep neural networks","score":0.40619999170303345},{"id":"https://openalex.org/keywords/deep-belief-network","display_name":"Deep belief network","score":0.3880999982357025},{"id":"https://openalex.org/keywords/adaptive-learning","display_name":"Adaptive learning","score":0.2896000146865845}],"concepts":[{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.7949000000953674},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.777400016784668},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.7545999884605408},{"id":"https://openalex.org/C2776214188","wikidata":"https://www.wikidata.org/wiki/Q408386","display_name":"Inference","level":2,"score":0.6496999859809875},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5421000123023987},{"id":"https://openalex.org/C2984842247","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep neural networks","level":3,"score":0.40619999170303345},{"id":"https://openalex.org/C97385483","wikidata":"https://www.wikidata.org/wiki/Q16954980","display_name":"Deep belief network","level":3,"score":0.3880999982357025},{"id":"https://openalex.org/C125014702","wikidata":"https://www.wikidata.org/wiki/Q4680749","display_name":"Adaptive learning","level":2,"score":0.2896000146865845},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.2782000005245209},{"id":"https://openalex.org/C3018790387","wikidata":"https://www.wikidata.org/wiki/Q869010","display_name":"Hybrid learning","level":2,"score":0.2476000040769577}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1016/j.eswa.2026.132994","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.eswa.2026.132994","pdf_url":null,"source":{"id":"https://openalex.org/S13144211","display_name":"Expert Systems with Applications","issn_l":"0957-4174","issn":["0957-4174","1873-6793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems with Applications","raw_type":"journal-article"}],"best_oa_location":{"id":"doi:10.1016/j.eswa.2026.132994","is_oa":true,"landing_page_url":"https://doi.org/10.1016/j.eswa.2026.132994","pdf_url":null,"source":{"id":"https://openalex.org/S13144211","display_name":"Expert Systems with Applications","issn_l":"0957-4174","issn":["0957-4174","1873-6793"],"is_oa":false,"is_in_doaj":false,"is_core":true,"host_organization":"https://openalex.org/P4310320990","host_organization_name":"Elsevier BV","host_organization_lineage":["https://openalex.org/P4310320990"],"host_organization_lineage_names":["Elsevier BV"],"type":"journal"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"Expert Systems with Applications","raw_type":"journal-article"},"sustainable_development_goals":[],"awards":[],"funders":[{"id":"https://openalex.org/F4320320879","display_name":"Deutsche Forschungsgemeinschaft","ror":"https://ror.org/018mejw64"}],"has_content":{"grobid_xml":false,"pdf":false},"content_urls":null,"referenced_works_count":54,"referenced_works":["https://openalex.org/W1888355991","https://openalex.org/W1988395759","https://openalex.org/W1992270151","https://openalex.org/W1995995344","https://openalex.org/W2019388858","https://openalex.org/W2058173171","https://openalex.org/W2508365795","https://openalex.org/W2563902504","https://openalex.org/W2795288700","https://openalex.org/W2798718204","https://openalex.org/W2902471886","https://openalex.org/W2943613385","https://openalex.org/W2950243778","https://openalex.org/W2956961633","https://openalex.org/W2962738377","https://openalex.org/W2963706338","https://openalex.org/W2968458585","https://openalex.org/W2970812663","https://openalex.org/W2979828430","https://openalex.org/W3009710013","https://openalex.org/W3016247415","https://openalex.org/W3022255990","https://openalex.org/W3034714729","https://openalex.org/W3046641708","https://openalex.org/W3080158435","https://openalex.org/W3091552653","https://openalex.org/W3115683849","https://openalex.org/W3124155640","https://openalex.org/W3152912788","https://openalex.org/W3163428972","https://openalex.org/W3178986468","https://openalex.org/W4220851616","https://openalex.org/W4221097574","https://openalex.org/W4224272835","https://openalex.org/W4280539170","https://openalex.org/W4282025272","https://openalex.org/W4313049093","https://openalex.org/W4321327316","https://openalex.org/W4322505088","https://openalex.org/W4367395647","https://openalex.org/W4377971276","https://openalex.org/W4384338138","https://openalex.org/W4386848513","https://openalex.org/W4388043068","https://openalex.org/W4389882468","https://openalex.org/W4390268027","https://openalex.org/W4392014094","https://openalex.org/W4392649932","https://openalex.org/W4392746128","https://openalex.org/W4400292898","https://openalex.org/W4402547213","https://openalex.org/W4404980639","https://openalex.org/W4407723236","https://openalex.org/W7154839306"],"related_works":[],"abstract_inverted_index":{"Accurately":[0],"predicting":[1,201],"insurance":[2,7],"claims":[3,64,156],"is":[4,47,79,138,164,182,190,206],"crucial":[5],"for":[6,26,30,62,151,174],"companies":[8],"as":[9,120],"it":[10],"directly":[11],"impacts":[12],"cash":[13],"flow,":[14],"pricing":[15],"strategies":[16],"and":[17,59,88,110,144,192],"overall":[18],"profitability.":[19],"This":[20],"paper":[21],"addresses":[22],"the":[23,36,39,51,72,85,99,108,128,142,148,155,184,193,197,202],"critical":[24],"need":[25],"distributional":[27],"forecasting,":[28],"particularly":[29],"regulatory":[31],"requirements":[32],"that":[33,49,166],"necessitate":[34],"understanding":[35],"variability":[37],"of":[38,84,107,123,147,186,196],"claims.":[40],"To":[41],"this":[42],"end,":[43],"a":[44,75,95,115,121,132,160],"hybrid":[45],"model":[46,137],"proposed":[48],"integrates":[50],"chain":[52],"ladder":[53],"(CL)":[54],"method,":[55],"machine":[56],"learning":[57,125],"(ML)":[58],"fuzzy":[60],"logic":[61],"enhanced":[63],"reserve":[65],"prediction":[66],"through":[67],"three":[68],"major":[69],"modules.":[70],"In":[71],"first":[73],"module,":[74],"multi-layer":[76],"perceptron":[77],"(MLP)":[78],"employed":[80,139],"to":[81,94,140],"capture":[82],"patterns":[83],"losses\u2019":[86],"location,":[87],"then,":[89],"CL":[90],"estimations":[91],"are":[92,112],"imported":[93],"second":[96],"module":[97],"using":[98],"adaptive":[100],"neuro-fuzzy":[101],"inference":[102],"system":[103],"(ANFIS).":[104],"The":[105],"outputs":[106],"MLP":[109],"ANFIS":[111],"integrated":[113],"into":[114],"recurrent":[116],"neural":[117,175],"network":[118],"(RNN),":[119],"kind":[122],"ensemble":[124],"approach,":[126],"in":[127,154,199],"third":[129],"module.":[130],"Specifically,":[131],"long":[133],"short-term":[134],"memory":[135],"(LSTM)":[136],"obtain":[141],"mean":[143],"standard":[145],"deviation":[146],"loss":[149,204],"distribution":[150],"each":[152],"cell":[153],"development":[157],"triangle.":[158],"Moreover,":[159],"module-wise":[161],"explainability":[162],"strategy":[163],"adopted":[165],"combines":[167],"intrinsically":[168],"interpretable":[169],"components":[170,189],"with":[171],"post-hoc":[172],"analyses":[173],"networks.":[176],"Through":[177],"comprehensive":[178],"simulations,":[179],"parameter":[180],"optimization":[181],"addressed,":[183],"importance":[185],"its":[187],"key":[188],"validated,":[191],"competitive":[194],"performance":[195],"method":[198],"accurately":[200],"outstanding":[203],"liabilities":[205],"demonstrated.":[207]},"counts_by_year":[],"updated_date":"2026-06-27T06:13:17.587685","created_date":"2026-06-05T00:00:00"}