{"id":"https://openalex.org/W7167904086","doi":"https://doi.org/10.48550/arxiv.2607.07858","title":"Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting","display_name":"Agentic AI and Retrieval-Augmented Models in Straight-Through Underwriting","publication_year":2026,"publication_date":"2026-07-08","ids":{"openalex":"https://openalex.org/W7167904086","doi":"https://doi.org/10.48550/arxiv.2607.07858"},"language":null,"primary_location":{"id":"doi:10.48550/arxiv.2607.07858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"type":"preprint","indexed_in":["datacite"],"open_access":{"is_oa":true,"oa_status":"green","oa_url":"https://doi.org/10.48550/arxiv.2607.07858","any_repository_has_fulltext":true},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5140326157","display_name":"Robert Richardson","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Richardson, Robert","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5021497920","display_name":"Josh Meyers","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Meyers, Josh","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"middle","author":{"id":"https://openalex.org/A5058501967","display_name":"Brian Hartman","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Hartman, Brian","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]},{"author_position":"last","author":{"id":"https://openalex.org/A5105032410","display_name":"David Sandberg","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Sandberg, David","raw_affiliation_strings":[],"raw_orcid":null,"affiliations":[]}],"institutions":[],"countries_distinct_count":0,"institutions_distinct_count":0,"corresponding_author_ids":[],"corresponding_institution_ids":[],"apc_list":null,"apc_paid":null,"fwci":null,"has_fulltext":false,"cited_by_count":0,"citation_normalized_percentile":null,"cited_by_percentile_year":null,"biblio":{"volume":null,"issue":null,"first_page":null,"last_page":null},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.08910000324249268,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},"topics":[{"id":"https://openalex.org/T10883","display_name":"Ethics and Social Impacts of AI","score":0.08910000324249268,"subfield":{"id":"https://openalex.org/subfields/3311","display_name":"Safety Research"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}},{"id":"https://openalex.org/T11918","display_name":"Forecasting Techniques and Applications","score":0.05469999834895134,"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/T13643","display_name":"Artificial Intelligence in Law","score":0.05050000175833702,"subfield":{"id":"https://openalex.org/subfields/3320","display_name":"Political Science and International Relations"},"field":{"id":"https://openalex.org/fields/33","display_name":"Social Sciences"},"domain":{"id":"https://openalex.org/domains/2","display_name":"Social Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/underwriting","display_name":"Underwriting","score":0.8812000155448914},{"id":"https://openalex.org/keywords/construct","display_name":"Construct (python library)","score":0.5638999938964844},{"id":"https://openalex.org/keywords/pipeline","display_name":"Pipeline (software)","score":0.5342000126838684},{"id":"https://openalex.org/keywords/focus","display_name":"Focus (optics)","score":0.46160000562667847},{"id":"https://openalex.org/keywords/reflection","display_name":"Reflection (computer programming)","score":0.4366999864578247},{"id":"https://openalex.org/keywords/automation","display_name":"Automation","score":0.40540000796318054},{"id":"https://openalex.org/keywords/face","display_name":"Face (sociological concept)","score":0.33970001339912415}],"concepts":[{"id":"https://openalex.org/C26503482","wikidata":"https://www.wikidata.org/wiki/Q1898080","display_name":"Underwriting","level":2,"score":0.8812000155448914},{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.6279000043869019},{"id":"https://openalex.org/C2780801425","wikidata":"https://www.wikidata.org/wiki/Q5164392","display_name":"Construct (python library)","level":2,"score":0.5638999938964844},{"id":"https://openalex.org/C43521106","wikidata":"https://www.wikidata.org/wiki/Q2165493","display_name":"Pipeline (software)","level":2,"score":0.5342000126838684},{"id":"https://openalex.org/C192209626","wikidata":"https://www.wikidata.org/wiki/Q190909","display_name":"Focus (optics)","level":2,"score":0.46160000562667847},{"id":"https://openalex.org/C65682993","wikidata":"https://www.wikidata.org/wiki/Q1056451","display_name":"Reflection (computer programming)","level":2,"score":0.4366999864578247},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.427700012922287},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.42100000381469727},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.41019999980926514},{"id":"https://openalex.org/C115901376","wikidata":"https://www.wikidata.org/wiki/Q184199","display_name":"Automation","level":2,"score":0.40540000796318054},{"id":"https://openalex.org/C2779304628","wikidata":"https://www.wikidata.org/wiki/Q3503480","display_name":"Face (sociological concept)","level":2,"score":0.33970001339912415},{"id":"https://openalex.org/C2776035091","wikidata":"https://www.wikidata.org/wiki/Q7928819","display_name":"Viewpoints","level":2,"score":0.32899999618530273},{"id":"https://openalex.org/C107327155","wikidata":"https://www.wikidata.org/wiki/Q330268","display_name":"Decision support system","level":2,"score":0.3253999948501587},{"id":"https://openalex.org/C112930515","wikidata":"https://www.wikidata.org/wiki/Q4389547","display_name":"Risk analysis (engineering)","level":1,"score":0.3122999966144562},{"id":"https://openalex.org/C2767350","wikidata":"https://www.wikidata.org/wiki/Q6662173","display_name":"Business intelligence","level":2,"score":0.3091999888420105},{"id":"https://openalex.org/C56739046","wikidata":"https://www.wikidata.org/wiki/Q192060","display_name":"Knowledge management","level":1,"score":0.3009999990463257},{"id":"https://openalex.org/C162118730","wikidata":"https://www.wikidata.org/wiki/Q1128453","display_name":"Actuarial science","level":1,"score":0.2973000109195709},{"id":"https://openalex.org/C2778572836","wikidata":"https://www.wikidata.org/wiki/Q380933","display_name":"Space (punctuation)","level":2,"score":0.28949999809265137},{"id":"https://openalex.org/C517642484","wikidata":"https://www.wikidata.org/wiki/Q2388514","display_name":"Intelligence analysis","level":2,"score":0.27950000762939453},{"id":"https://openalex.org/C148220186","wikidata":"https://www.wikidata.org/wiki/Q7111912","display_name":"Outcome (game theory)","level":2,"score":0.26339998841285706},{"id":"https://openalex.org/C157763539","wikidata":"https://www.wikidata.org/wiki/Q6806621","display_name":"Medical underwriting","level":5,"score":0.2508000135421753}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.48550/arxiv.2607.07858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":null,"raw_source_name":null,"raw_type":"Preprint"}],"best_oa_location":{"id":"doi:10.48550/arxiv.2607.07858","is_oa":true,"landing_page_url":"https://doi.org/10.48550/arxiv.2607.07858","pdf_url":null,"source":{"id":"https://openalex.org/S4306400194","display_name":"arXiv (Cornell University)","issn_l":"2331-8422","issn":["2331-8422"],"is_oa":true,"is_in_doaj":false,"is_core":false,"host_organization":"https://openalex.org/I205783295","host_organization_name":"Cornell University","host_organization_lineage":["https://openalex.org/I205783295"],"host_organization_lineage_names":[],"type":"repository"},"license":"cc-by","license_id":"https://openalex.org/licenses/cc-by","version":null,"is_accepted":false,"is_published":false,"raw_source_name":null,"raw_type":"Preprint"},"sustainable_development_goals":[{"score":0.6501631736755371,"id":"https://metadata.un.org/sdg/16","display_name":"Peace, Justice and strong institutions"}],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":0,"referenced_works":[],"related_works":[],"abstract_inverted_index":{"Artificial":[0],"intelligence":[1],"(AI)":[2],"is":[3],"beginning":[4],"to":[5,37],"reshape":[6],"actuarial":[7,65],"practice,":[8],"particularly":[9],"in":[10,155],"domains":[11],"that":[12,31,49,133],"require":[13],"reasoning":[14],"over":[15],"unstructured":[16],"documents,":[17],"heterogeneous":[18],"data":[19,138],"sources,":[20],"and":[21,45,54,71,88,112,126,140,157,163],"regulated":[22],"decision":[23,79],"workflows.":[24],"Actuaries":[25],"now":[26],"face":[27],"a":[28,75,106,118,122,128],"design":[29],"space":[30],"ranges":[32],"from":[33],"traditional":[34],"rule-based":[35],"automation":[36],"large":[38],"language":[39],"models":[40],"(LLMs),":[41],"retrieval-augmented":[42],"generation":[43],"(RAG),":[44],"multi-agent":[46,129],"``agentic''":[47],"systems":[48],"plan,":[50],"retrieve,":[51],"call":[52],"tools,":[53],"reflect.":[55],"This":[56],"paper":[57],"examines":[58],"how":[59],"these":[60,83],"emerging":[61],"architectures":[62],"can":[63],"support":[64],"priorities":[66],"such":[67],"as":[68],"transparency,":[69],"auditability,":[70],"human-in-the-loop":[72],"governance,":[73],"with":[74,151],"focus":[76],"on":[77],"straight-through":[78,95,170],"processes.":[80],"To":[81],"make":[82],"ideas":[84],"concrete,":[85],"we":[86],"develop":[87],"analyze":[89],"an":[90],"agentic":[91,146],"AI":[92],"framework":[93],"for":[94],"underwriting":[96,115],"of":[97],"small":[98],"commercial":[99],"Business":[100],"Owner":[101],"Policies":[102],"(BOPs).":[103],"We":[104],"construct":[105],"synthetic":[107],"but":[108],"realistic":[109],"experimental":[110],"environment":[111],"compare":[113],"three":[114],"pipelines:":[116],"(i)":[117],"single-LLM":[119],"baseline,":[120],"(ii)":[121],"naive":[123],"RAG":[124],"system,":[125],"(iii)":[127],"``Agentic":[130],"RAG''":[131],"pipeline":[132],"combines":[134],"targeted":[135],"retrieval,":[136],"third-party":[137],"checks,":[139],"explicit":[141],"multi-step":[142,156],"rule":[143],"evaluation.":[144],"The":[145],"system":[147],"performs":[148],"best":[149],"overall,":[150],"the":[152,166],"largest":[153],"gains":[154],"missing-information":[158],"scenarios,":[159],"where":[160],"structured":[161],"retrieval":[162],"reflection":[164],"help":[165],"model":[167],"avoid":[168],"unsupported":[169],"decisions.":[171]},"counts_by_year":[],"updated_date":"2026-07-28T07:46:37.118299","created_date":"2026-07-11T00:00:00"}
