{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T08:14:58Z","timestamp":1783152898368,"version":"3.54.6"},"reference-count":66,"publisher":"Elsevier BV","license":[{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/tdm\/userlicense\/1.0\/"},{"start":{"date-parts":[[2026,12,1]],"date-time":"2026-12-01T00:00:00Z","timestamp":1796083200000},"content-version":"tdm","delay-in-days":0,"URL":"https:\/\/www.elsevier.com\/legal\/tdmrep-license"},{"start":{"date-parts":[[2026,5,23]],"date-time":"2026-05-23T00:00:00Z","timestamp":1779494400000},"content-version":"vor","delay-in-days":0,"URL":"http:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"DOI":"10.13039\/501100001659","name":"German Research Foundation","doi-asserted-by":"publisher","award":["409030527"],"award-info":[{"award-number":["409030527"]}],"id":[{"id":"10.13039\/501100001659","id-type":"DOI","asserted-by":"publisher"}]}],"content-domain":{"domain":["elsevier.com","sciencedirect.com"],"crossmark-restriction":true},"short-container-title":["Expert Systems with Applications"],"published-print":{"date-parts":[[2026,12]]},"DOI":"10.1016\/j.eswa.2026.132994","type":"journal-article","created":{"date-parts":[[2026,6,4]],"date-time":"2026-06-04T23:51:09Z","timestamp":1780617069000},"page":"132994","update-policy":"https:\/\/doi.org\/10.1016\/elsevier_cm_policy","source":"Crossref","is-referenced-by-count":0,"special_numbering":"PC","title":["An explainable hybrid adaptive neuro-fuzzy inference system and deep learning framework for stochastic claims reserving"],"prefix":"10.1016","volume":"331","author":[{"ORCID":"https:\/\/orcid.org\/0000-0002-4265-3996","authenticated-orcid":false,"given":"Arne","family":"Johannssen","sequence":"first","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-1569-9809","authenticated-orcid":false,"given":"Ali","family":"Yeganeh","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-4105-7033","authenticated-orcid":false,"given":"Nataliya","family":"Chukhrova","sequence":"additional","affiliation":[],"role":[{"vocabulary":"crossref","role":"author"}]}],"member":"78","reference":[{"key":"10.1016\/j.eswa.2026.132994_b0005","first-page":"144","article-title":"Stochastic loss reserving with mixture density neural networks","volume":"105","author":"Al-Mudafer","year":"2022","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0010","doi-asserted-by":"crossref","first-page":"1194","DOI":"10.3390\/pr9071194","article-title":"Optimized ANFIS model using Aquila optimizer for oil production forecasting","volume":"9","author":"AlRassas","year":"2021","journal-title":"Processes"},{"key":"10.1016\/j.eswa.2026.132994_b0015","first-page":"113","article-title":"Hybrid fuzzy least-squares regression analysis in claims reserving with geometric separation method","volume":"47","author":"Apaydin","year":"2010","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0020","first-page":"296","article-title":"SynthETIC: An individual insurance claim simulator with feature control","volume":"100","author":"Avanzi","year":"2021","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0025","doi-asserted-by":"crossref","first-page":"971","DOI":"10.1080\/03461238.2024.2365392","article-title":"Ensemble distributional forecasting for insurance loss reserving","volume":"2024","author":"Avanzi","year":"2024","journal-title":"Scandinavian Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0030","article-title":"Continuous-time modeling and bootstrap for chain-ladder reserving","volume":"126","author":"Baradel","year":"2026","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0035","doi-asserted-by":"crossref","first-page":"22","DOI":"10.3390\/risks8010022","article-title":"Prediction of claims in export credit finance: A comparison of four machine learning techniques","volume":"8","author":"B\u00e4rtl","year":"2020","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0040","doi-asserted-by":"crossref","first-page":"1127","DOI":"10.1002\/asmb.2455","article-title":"A machine learning approach for individual claims reserving in insurance","volume":"35","author":"Baudry","year":"2019","journal-title":"Applied Stochastic Models in Business and Industry"},{"key":"10.1016\/j.eswa.2026.132994_b0045","doi-asserted-by":"crossref","first-page":"29","DOI":"10.1017\/asb.2024.28","article-title":"Individual claims reserving using the Aalen\u2013Johansen estimator","volume":"55","author":"Bladt","year":"2024","journal-title":"ASTIN Bulletin"},{"key":"10.1016\/j.eswa.2026.132994_b0050","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/risks9010004","article-title":"Machine learning in P&C insurance: A review for pricing and reserving","volume":"9","author":"Blier-Wong","year":"2021","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0055","doi-asserted-by":"crossref","first-page":"55","DOI":"10.1007\/s13385-022-00314-4","article-title":"Micro-level prediction of outstanding claim counts based on novel mixture models and neural networks","volume":"13","author":"B\u00fccher","year":"2023","journal-title":"European Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0060","doi-asserted-by":"crossref","first-page":"6135","DOI":"10.1016\/j.eswa.2008.07.019","article-title":"An adaptive neuro-fuzzy inference system (ANFIS) model for wire-EDM","volume":"36","author":"\u00c7ayda\u015f","year":"2009","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132994_b0065","doi-asserted-by":"crossref","first-page":"382","DOI":"10.1002\/asmb.2750","article-title":"Micro\u2010level reserving for general insurance claims using a long short\u2010term memory network","volume":"39","author":"Chaoubi","year":"2023","journal-title":"Applied Stochastic Models in Business and Industry"},{"key":"10.1016\/j.eswa.2026.132994_b0070","doi-asserted-by":"crossref","DOI":"10.1016\/j.asoc.2019.105708","article-title":"Fuzzy regression analysis: Systematic review and bibliography","volume":"84","author":"Chukhrova","year":"2019","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.132994_b0075","first-page":"158","article-title":"A hierarchical reserving model for reported non-life insurance claims","volume":"104","author":"Crevecoeur","year":"2022","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0080","doi-asserted-by":"crossref","first-page":"470","DOI":"10.1080\/10920277.2021.2022497","article-title":"Using machine learning to better model long-term care insurance claims","volume":"26","author":"Cummings","year":"2022","journal-title":"North American Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0110","doi-asserted-by":"crossref","first-page":"3091","DOI":"10.1016\/j.fss.2006.07.003","article-title":"Calculating insurance claim reserves with fuzzy regression","volume":"157","author":"de Andr\u00e9s S\u00e1nchez","year":"2006","journal-title":"Fuzzy sets and systems"},{"key":"10.1016\/j.eswa.2026.132994_b0085","first-page":"145","article-title":"Claim reserving with fuzzy regression and Taylor\u2019s geometric separation method","volume":"40","author":"de Andr\u00e9s-S\u00e1nchez","year":"2007","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0090","doi-asserted-by":"crossref","first-page":"2435","DOI":"10.1016\/j.asoc.2012.03.033","article-title":"Claim reserving with fuzzy regression and the two ways of ANOVA","volume":"12","author":"de Andr\u00e9s-S\u00e1nchez","year":"2012","journal-title":"Applied Soft Computing"},{"key":"10.1016\/j.eswa.2026.132994_b0095","doi-asserted-by":"crossref","first-page":"845","DOI":"10.3390\/math12060845","article-title":"Calculating insurance claim reserves with an intuitionistic fuzzy chain-ladder method","volume":"12","author":"de Andr\u00e9s-S\u00e1nchez","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.132994_b0100","doi-asserted-by":"crossref","first-page":"184","DOI":"10.3390\/axioms13030184","article-title":"Fitting insurance claim reserves with two-way ANOVA and intuitionistic fuzzy regression","volume":"13","author":"de Andr\u00e9s-S\u00e1nchez","year":"2024","journal-title":"Axioms"},{"key":"10.1016\/j.eswa.2026.132994_b0105","doi-asserted-by":"crossref","first-page":"665","DOI":"10.1046\/j.0022-4367.2003.00070.x","article-title":"Applications of fuzzy regression in actuarial analysis","volume":"70","author":"de Andr\u00e9s-S\u00e1nchez","year":"2003","journal-title":"Journal of Risk and Insurance"},{"key":"10.1016\/j.eswa.2026.132994_b0115","doi-asserted-by":"crossref","first-page":"102","DOI":"10.3390\/risks7040102","article-title":"Claim watching and individual claims reserving using classification and regression trees","volume":"7","author":"De Felice","year":"2019","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0120","doi-asserted-by":"crossref","first-page":"221","DOI":"10.3390\/risks11120221","article-title":"Stochastic chain-ladder reserving with modeled general inflation","volume":"11","author":"De Felice","year":"2023","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0125","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03461238.2021.1921836","article-title":"Collective reserving using individual claims data","volume":"2022","author":"Delong","year":"2022","journal-title":"Scandinavian Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0130","doi-asserted-by":"crossref","first-page":"33","DOI":"10.3390\/risks8020033","article-title":"Neural networks for the joint development of individual payments and claim incurred","volume":"8","author":"Delong","year":"2020","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0135","doi-asserted-by":"crossref","first-page":"79","DOI":"10.3390\/risks7030079","article-title":"Individual loss reserving using a gradient boosting-based approach","volume":"7","author":"Duval","year":"2019","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0140","doi-asserted-by":"crossref","first-page":"4","DOI":"10.3390\/risks12010004","article-title":"Advancing the use of deep learning in loss reserving: A generalized DeepTriangle approach","volume":"12","author":"Feng","year":"2024","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0145","doi-asserted-by":"crossref","first-page":"541","DOI":"10.1007\/s13385-021-00271-4","article-title":"An individual claims reserving model for reported claims","volume":"11","author":"Gabrielli","year":"2021","journal-title":"European Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0150","doi-asserted-by":"crossref","first-page":"1","DOI":"10.1080\/03461238.2019.1633394","article-title":"Neural network embedding of the over-dispersed Poisson reserving model","volume":"2020","author":"Gabrielli","year":"2020","journal-title":"Scandinavian Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0155","doi-asserted-by":"crossref","first-page":"29","DOI":"10.3390\/risks6020029","article-title":"An individual claims history simulation machine","volume":"6","author":"Gabrielli","year":"2018","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0160","first-page":"572","article-title":"Dispersion modelling of outstanding claims with double Poisson regression models","volume":"101","author":"Gao","year":"2021","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0165","first-page":"96","article-title":"Combining chain-ladder claims reserving with fuzzy numbers","volume":"55","author":"Heberle","year":"2014","journal-title":"Insurance: Mathematics and Economics"},{"key":"10.1016\/j.eswa.2026.132994_b0170","doi-asserted-by":"crossref","first-page":"303","DOI":"10.1017\/S1748499516000117","article-title":"The fuzzy Bornhuetter\u2013Ferguson method: An approach with fuzzy numbers","volume":"10","author":"Heberle","year":"2016","journal-title":"Annals of Actuarial Science"},{"key":"10.1016\/j.eswa.2026.132994_b0175","doi-asserted-by":"crossref","first-page":"657","DOI":"10.1016\/j.ejor.2015.09.039","article-title":"Asymptotic behaviors of stochastic reserving: Aggregate versus individual models","volume":"249","author":"Huang","year":"2016","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.eswa.2026.132994_b0180","doi-asserted-by":"crossref","DOI":"10.1016\/j.mlwa.2026.100903","article-title":"Real-time monitoring of insurance claims predictions using hybrid neuro-fuzzy control charts with applications to health insurance","volume":"24","author":"Johannssen","year":"2026","journal-title":"Machine Learning with Applications"},{"key":"10.1016\/j.eswa.2026.132994_b0185","unstructured":"Keras. (2025). https:\/\/keras.io\/api\/models\/sequential\/."},{"key":"10.1016\/j.eswa.2026.132994_b0190","doi-asserted-by":"crossref","first-page":"343","DOI":"10.14317\/jami.2014.343","article-title":"Fuzzy regression towards a general insurance application","volume":"32","author":"Kim","year":"2014","journal-title":"Journal of Applied Mathematics & Informatics"},{"key":"10.1016\/j.eswa.2026.132994_b0195","doi-asserted-by":"crossref","first-page":"97","DOI":"10.3390\/risks7030097","article-title":"Deeptriangle: A deep learning approach to loss reserving","volume":"7","author":"Kuo","year":"2019","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0200","doi-asserted-by":"crossref","first-page":"394","DOI":"10.1017\/S1748499520000263","article-title":"A practical support vector regression algorithm and kernel function for attritional general insurance loss estimation","volume":"15","author":"Kwasa","year":"2021","journal-title":"Annals of Actuarial Science"},{"key":"10.1016\/j.eswa.2026.132994_b0205","doi-asserted-by":"crossref","first-page":"34","DOI":"10.1080\/03461238.2020.1793218","article-title":"Individual reserving and nonparametric estimation of claim amounts subject to large reporting delays","volume":"2021","author":"Lopez","year":"2021","journal-title":"Scandinavian Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0210","doi-asserted-by":"crossref","first-page":"635","DOI":"10.3390\/math12050635","article-title":"Potential applications of explainable artificial intelligence to actuarial problems","volume":"12","author":"Lozano-Murcia","year":"2024","journal-title":"Mathematics"},{"key":"10.1016\/j.eswa.2026.132994_b0215","unstructured":"MATHWORKS. (2024). https:\/\/www.mathworks.com\/help\/fuzzy\/fuzzy-inference-system-modeling.html."},{"key":"10.1016\/j.eswa.2026.132994_b0220","doi-asserted-by":"crossref","first-page":"126","DOI":"10.1016\/j.patrec.2025.02.013","article-title":"Fractional concepts in neural networks: Enhancing activation functions","volume":"190","author":"Molek","year":"2025","journal-title":"Pattern Recognition Letters"},{"key":"10.1016\/j.eswa.2026.132994_b0225","doi-asserted-by":"crossref","first-page":"3081","DOI":"10.1007\/s00477-022-02181-7","article-title":"Deep learning-based uncertainty quantification of groundwater level predictions","volume":"36","author":"Nourani","year":"2022","journal-title":"Stochastic Environmental Research and Risk Assessment"},{"key":"10.1016\/j.eswa.2026.132994_b0230","doi-asserted-by":"crossref","first-page":"95","DOI":"10.3390\/risks7030095","article-title":"Penalising unexplainability in neural networks for predicting payments per claim incurred","volume":"7","author":"Poon","year":"2019","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0235","doi-asserted-by":"crossref","first-page":"164","DOI":"10.3390\/risks11090164","article-title":"Machine learning in forecasting motor insurance claims","volume":"11","author":"Poufinas","year":"2023","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0240","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2020.113782","article-title":"Stochastic reserving with a stacked model based on a hybridized Artificial Neural Network","volume":"163","author":"Ramos-P\u00e9rez","year":"2021","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132994_b0245","doi-asserted-by":"crossref","first-page":"e21","DOI":"10.1017\/S1357321722000162","article-title":"Mind the gap \u2013 safely incorporating deep learning models into the actuarial toolkit","volume":"27","author":"Richman","year":"2022","journal-title":"British Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0250","doi-asserted-by":"crossref","first-page":"82","DOI":"10.3390\/risks7030082","article-title":"Loss reserving models: Granular and machine learning forms","volume":"7","author":"Taylor","year":"2019","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0255","doi-asserted-by":"crossref","DOI":"10.1016\/j.atmosenv.2023.119677","article-title":"Quantification of COVID-19 impacts on NO2 and O3: Systematic model selection and hyperparameter optimization on AI-based meteorological-normalization methods","volume":"301","author":"Wong","year":"2023","journal-title":"Atmospheric Environment"},{"key":"10.1016\/j.eswa.2026.132994_b0260","doi-asserted-by":"crossref","first-page":"930","DOI":"10.1007\/s40815-018-0564-6","article-title":"A fuzzy least-squares estimation of a hybrid log-poisson regression and its goodness of fit for optimal loss reserves in insurance","volume":"21","author":"Woundjiagu\u00e9","year":"2019","journal-title":"International Journal of Fuzzy Systems"},{"key":"10.1016\/j.eswa.2026.132994_b0265","doi-asserted-by":"crossref","DOI":"10.1155\/2019\/1393946","article-title":"An estimation of a hybrid log-poisson regression using a quadratic optimization program for optimal loss reserving in insurance","volume":"2019","author":"Woundjiagu\u00e9","year":"2019","journal-title":"Advances in Fuzzy Systems"},{"key":"10.1016\/j.eswa.2026.132994_b0270","doi-asserted-by":"crossref","first-page":"465","DOI":"10.1080\/03461238.2018.1428681","article-title":"Machine learning in individual claims reserving","volume":"2018","author":"W\u00fcthrich","year":"2018","journal-title":"Scandinavian Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0275","doi-asserted-by":"crossref","first-page":"407","DOI":"10.1007\/s13385-018-0184-4","article-title":"Neural networks applied to chain\u2013ladder reserving","volume":"8","author":"W\u00fcthrich","year":"2018","journal-title":"European Actuarial Journal"},{"key":"10.1016\/j.eswa.2026.132994_b0280","series-title":"Stochastic claims reserving methods in insurance","author":"W\u00fcthrich","year":"2008"},{"key":"10.1016\/j.eswa.2026.132994_b0285","series-title":"Statistical foundations of actuarial learning and its applications","author":"W\u00fcthrich","year":"2023"},{"key":"10.1016\/j.eswa.2026.132994_b0290","doi-asserted-by":"crossref","first-page":"10843","DOI":"10.1109\/TITS.2023.3276704","article-title":"Predicting urban region heat via learning arrive-stay-leave behaviors of private cars","volume":"24","author":"Xiao","year":"2023","journal-title":"IEEE Transactions on Intelligent Transportation Systems"},{"key":"10.1016\/j.eswa.2026.132994_b0295","doi-asserted-by":"crossref","DOI":"10.1016\/j.eswa.2022.116780","article-title":"Feature extraction of auto insurance size of loss data using functional principal component analysis","volume":"198","author":"Xie","year":"2022","journal-title":"Expert Systems with Applications"},{"key":"10.1016\/j.eswa.2026.132994_b0300","doi-asserted-by":"crossref","first-page":"131","DOI":"10.3390\/risks11070131","article-title":"AutoReserve: A web-based tool for personal auto insurance loss reserving with classical and machine learning methods","volume":"11","author":"Xiong","year":"2023","journal-title":"Risks"},{"key":"10.1016\/j.eswa.2026.132994_b0305","doi-asserted-by":"crossref","first-page":"1026","DOI":"10.1080\/07350015.2023.2277172","article-title":"Functional-coefficient quantile regression for panel data with latent group structure","volume":"42","author":"Yang","year":"2024","journal-title":"Journal of Business & Economic Statistics"},{"key":"10.1016\/j.eswa.2026.132994_b0310","doi-asserted-by":"crossref","first-page":"16321","DOI":"10.1007\/s00521-023-08483-3","article-title":"Employing machine learning techniques in monitoring autocorrelated profiles","volume":"35","author":"Yeganeh","year":"2023","journal-title":"Neural Computing and Applications"},{"key":"10.1016\/j.eswa.2026.132994_b0315","doi-asserted-by":"crossref","first-page":"87","DOI":"10.1016\/j.neunet.2022.05.030","article-title":"Successfully and efficiently training deep multi-layer perceptrons with logistic activation function simply requires initializing the weights with an appropriate negative mean","volume":"153","author":"Yilmaz","year":"2022","journal-title":"Neural Networks"},{"key":"10.1016\/j.eswa.2026.132994_b0320","doi-asserted-by":"crossref","first-page":"85","DOI":"10.1016\/j.neucom.2020.09.030","article-title":"Fast training of deep LSTM networks with guaranteed stability for nonlinear system modeling","volume":"422","author":"Yu","year":"2021","journal-title":"Neurocomputing"},{"key":"10.1016\/j.eswa.2026.132994_b0325","doi-asserted-by":"crossref","first-page":"586","DOI":"10.1016\/j.ejor.2024.09.025","article-title":"Attention-based dynamic multilayer graph neural networks for loan default prediction","volume":"321","author":"Zandi","year":"2025","journal-title":"European Journal of Operational Research"},{"key":"10.1016\/j.eswa.2026.132994_b0330","doi-asserted-by":"crossref","first-page":"4864","DOI":"10.3390\/su12124864","article-title":"Neural-network-based dynamic distribution model of parking space under sharing and non-sharing modes","volume":"12","author":"Zhao","year":"2020","journal-title":"Sustainability"}],"container-title":["Expert Systems with Applications"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019056?httpAccept=text\/xml","content-type":"text\/xml","content-version":"vor","intended-application":"text-mining"},{"URL":"https:\/\/api.elsevier.com\/content\/article\/PII:S0957417426019056?httpAccept=text\/plain","content-type":"text\/plain","content-version":"vor","intended-application":"text-mining"}],"deposited":{"date-parts":[[2026,7,4]],"date-time":"2026-07-04T07:15:00Z","timestamp":1783149300000},"score":1,"resource":{"primary":{"URL":"https:\/\/linkinghub.elsevier.com\/retrieve\/pii\/S0957417426019056"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,12]]},"references-count":66,"alternative-id":["S0957417426019056"],"URL":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132994","relation":{},"ISSN":["0957-4174"],"issn-type":[{"value":"0957-4174","type":"print"}],"subject":[],"published":{"date-parts":[[2026,12]]},"assertion":[{"value":"Elsevier","name":"publisher","label":"This article is maintained by"},{"value":"An explainable hybrid adaptive neuro-fuzzy inference system and deep learning framework for stochastic claims reserving","name":"articletitle","label":"Article Title"},{"value":"Expert Systems with Applications","name":"journaltitle","label":"Journal Title"},{"value":"https:\/\/doi.org\/10.1016\/j.eswa.2026.132994","name":"articlelink","label":"CrossRef DOI link to publisher maintained version"},{"value":"article","name":"content_type","label":"Content Type"},{"value":"\u00a9 2026 The Author(s). Published by Elsevier Ltd.","name":"copyright","label":"Copyright"}],"article-number":"132994"}}