{"id":"https://openalex.org/W4391095288","doi":"https://doi.org/10.1109/bigdata59044.2023.10386232","title":"Hallucination-minimized Data-to-answer Framework for Financial Decision-makers","display_name":"Hallucination-minimized Data-to-answer Framework for Financial Decision-makers","publication_year":2023,"publication_date":"2023-12-15","ids":{"openalex":"https://openalex.org/W4391095288","doi":"https://doi.org/10.1109/bigdata59044.2023.10386232"},"language":"en","primary_location":{"id":"doi:10.1109/bigdata59044.2023.10386232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"},"type":"article","indexed_in":["crossref"],"open_access":{"is_oa":false,"oa_status":"closed","oa_url":null,"any_repository_has_fulltext":false},"authorships":[{"author_position":"first","author":{"id":"https://openalex.org/A5050360160","display_name":"Sohini Roychowdhury","orcid":"https://orcid.org/0000-0001-6416-9890"},"institutions":[{"id":"https://openalex.org/I4210099672","display_name":"Accenture (United States)","ror":"https://ror.org/013g16z83","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210099672"]}],"countries":["US"],"is_corresponding":true,"raw_author_name":"Sohini Roychowdhury","raw_affiliation_strings":["Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","Corporate Data and Analytics Office (CDAO), Accenture LLP, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","institution_ids":["https://openalex.org/I4210099672"]},{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP, USA","institution_ids":["https://openalex.org/I4210099672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5114120436","display_name":"Andr\u00e9s M. \u00c1lvarez","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099672","display_name":"Accenture (United States)","ror":"https://ror.org/013g16z83","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210099672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Andres Alvarez","raw_affiliation_strings":["Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","Corporate Data and Analytics Office (CDAO), Accenture LLP, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","institution_ids":["https://openalex.org/I4210099672"]},{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP, USA","institution_ids":["https://openalex.org/I4210099672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5110050608","display_name":"Brian C. J. Moore","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099672","display_name":"Accenture (United States)","ror":"https://ror.org/013g16z83","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210099672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Brian Moore","raw_affiliation_strings":["Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","Corporate Data and Analytics Office (CDAO), Accenture LLP, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","institution_ids":["https://openalex.org/I4210099672"]},{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP, USA","institution_ids":["https://openalex.org/I4210099672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5059594013","display_name":"Marko Krema","orcid":null},"institutions":[{"id":"https://openalex.org/I4210099672","display_name":"Accenture (United States)","ror":"https://ror.org/013g16z83","country_code":"US","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210099672"]}],"countries":["US"],"is_corresponding":false,"raw_author_name":"Marko Krema","raw_affiliation_strings":["Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","Corporate Data and Analytics Office (CDAO), Accenture LLP, USA"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP,USA","institution_ids":["https://openalex.org/I4210099672"]},{"raw_affiliation_string":"Corporate Data and Analytics Office (CDAO), Accenture LLP, USA","institution_ids":["https://openalex.org/I4210099672"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093272092","display_name":"Maria Paz Gelpi","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Maria Paz Gelpi","raw_affiliation_strings":["CDAO, Accenture,Argentina","CDAO, Accenture, Argentina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,Argentina","institution_ids":[]},{"raw_affiliation_string":"CDAO, Accenture, Argentina","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5053600176","display_name":"Punit Agrawal","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Punit Agrawal","raw_affiliation_strings":["CDAO, Accenture,India","CDAO, Accenture, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,India","institution_ids":[]},{"raw_affiliation_string":"CDAO, Accenture, India","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5109837394","display_name":"F.M. Le\u00f3n Rodr\u00edguez","orcid":null},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Federico Mart\u00edn Rodr\u00edguez","raw_affiliation_strings":["CDAO, Accenture,Argentina","CDAO, Accenture, Argentina"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,Argentina","institution_ids":[]},{"raw_affiliation_string":"CDAO, Accenture, Argentina","institution_ids":[]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5068756606","display_name":"\u00c1ngel Rodr\u00edguez","orcid":"https://orcid.org/0000-0002-4337-047X"},"institutions":[{"id":"https://openalex.org/I4210144599","display_name":"Accenture (Spain)","ror":"https://ror.org/04xcr6k92","country_code":"ES","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210144599"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"\u00c1ngel Rodr\u00edguez","raw_affiliation_strings":["CDAO, Accenture,Spain","CDAO, Accenture, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,Spain","institution_ids":["https://openalex.org/I4210144599"]},{"raw_affiliation_string":"CDAO, Accenture, Spain","institution_ids":["https://openalex.org/I4210144599"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5093272093","display_name":"Jos\u00e9 Ram\u00f3n Cabrejas","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144599","display_name":"Accenture (Spain)","ror":"https://ror.org/04xcr6k92","country_code":"ES","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210144599"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Jos\u00e9 Ram\u00f3n Cabrejas","raw_affiliation_strings":["CDAO, Accenture,Spain","CDAO, Accenture, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,Spain","institution_ids":["https://openalex.org/I4210144599"]},{"raw_affiliation_string":"CDAO, Accenture, Spain","institution_ids":["https://openalex.org/I4210144599"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5104198736","display_name":"Pablo Mart\u00ednez Serrano","orcid":null},"institutions":[{"id":"https://openalex.org/I4210144599","display_name":"Accenture (Spain)","ror":"https://ror.org/04xcr6k92","country_code":"ES","type":"company","lineage":["https://openalex.org/I4210093804","https://openalex.org/I4210144599"]}],"countries":["ES"],"is_corresponding":false,"raw_author_name":"Pablo Mart\u00ednez Serrano","raw_affiliation_strings":["CDAO, Accenture,Spain","CDAO, Accenture, Spain"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,Spain","institution_ids":["https://openalex.org/I4210144599"]},{"raw_affiliation_string":"CDAO, Accenture, Spain","institution_ids":["https://openalex.org/I4210144599"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5031139197","display_name":"Arijit Mukherjee","orcid":"https://orcid.org/0000-0001-5052-4476"},"institutions":[],"countries":[],"is_corresponding":false,"raw_author_name":"Arijit Mukherjee","raw_affiliation_strings":["CDAO, Accenture,India","CDAO, Accenture, India"],"raw_orcid":null,"affiliations":[{"raw_affiliation_string":"CDAO, Accenture,India","institution_ids":[]},{"raw_affiliation_string":"CDAO, Accenture, India","institution_ids":[]}]}],"institutions":[],"countries_distinct_count":2,"institutions_distinct_count":11,"corresponding_author_ids":["https://openalex.org/A5050360160"],"corresponding_institution_ids":["https://openalex.org/I4210099672"],"apc_list":null,"apc_paid":null,"fwci":2.6897,"has_fulltext":false,"cited_by_count":11,"citation_normalized_percentile":{"value":0.90691497,"is_in_top_1_percent":false,"is_in_top_10_percent":true},"cited_by_percentile_year":{"min":96,"max":99},"biblio":{"volume":null,"issue":null,"first_page":"4693","last_page":"4702"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11326","display_name":"Stock Market Forecasting Methods","score":0.9937999844551086,"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/T11326","display_name":"Stock Market Forecasting Methods","score":0.9937999844551086,"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/T10664","display_name":"Sentiment Analysis and Opinion Mining","score":0.9926000237464905,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}},{"id":"https://openalex.org/T13083","display_name":"Advanced Text Analysis Techniques","score":0.9919999837875366,"subfield":{"id":"https://openalex.org/subfields/1702","display_name":"Artificial Intelligence"},"field":{"id":"https://openalex.org/fields/17","display_name":"Computer Science"},"domain":{"id":"https://openalex.org/domains/3","display_name":"Physical Sciences"}}],"keywords":[{"id":"https://openalex.org/keywords/computer-science","display_name":"Computer science","score":0.795814037322998},{"id":"https://openalex.org/keywords/variety","display_name":"Variety (cybernetics)","score":0.7085689306259155},{"id":"https://openalex.org/keywords/metric","display_name":"Metric (unit)","score":0.4383738040924072},{"id":"https://openalex.org/keywords/table","display_name":"Table (database)","score":0.4242568612098694},{"id":"https://openalex.org/keywords/information-retrieval","display_name":"Information retrieval","score":0.40974679589271545},{"id":"https://openalex.org/keywords/data-science","display_name":"Data science","score":0.4085518717765808},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.35898351669311523},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.3529770076274872},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.2904188632965088}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.795814037322998},{"id":"https://openalex.org/C136197465","wikidata":"https://www.wikidata.org/wiki/Q1729295","display_name":"Variety (cybernetics)","level":2,"score":0.7085689306259155},{"id":"https://openalex.org/C176217482","wikidata":"https://www.wikidata.org/wiki/Q860554","display_name":"Metric (unit)","level":2,"score":0.4383738040924072},{"id":"https://openalex.org/C45235069","wikidata":"https://www.wikidata.org/wiki/Q278425","display_name":"Table (database)","level":2,"score":0.4242568612098694},{"id":"https://openalex.org/C23123220","wikidata":"https://www.wikidata.org/wiki/Q816826","display_name":"Information retrieval","level":1,"score":0.40974679589271545},{"id":"https://openalex.org/C2522767166","wikidata":"https://www.wikidata.org/wiki/Q2374463","display_name":"Data science","level":1,"score":0.4085518717765808},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.35898351669311523},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.3529770076274872},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.2904188632965088},{"id":"https://openalex.org/C21547014","wikidata":"https://www.wikidata.org/wiki/Q1423657","display_name":"Operations management","level":1,"score":0.0},{"id":"https://openalex.org/C162324750","wikidata":"https://www.wikidata.org/wiki/Q8134","display_name":"Economics","level":0,"score":0.0}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/bigdata59044.2023.10386232","is_oa":false,"landing_page_url":"https://doi.org/10.1109/bigdata59044.2023.10386232","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2023 IEEE International Conference on Big Data (BigData)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[{"score":0.5699999928474426,"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":37,"referenced_works":["https://openalex.org/W2171622735","https://openalex.org/W2988647680","https://openalex.org/W3030163527","https://openalex.org/W3202009528","https://openalex.org/W4225353277","https://openalex.org/W4292779060","https://openalex.org/W4306292967","https://openalex.org/W4311991106","https://openalex.org/W4322718191","https://openalex.org/W4361866125","https://openalex.org/W4378189609","https://openalex.org/W4381827124","https://openalex.org/W4384389802","https://openalex.org/W4384918448","https://openalex.org/W4385372663","https://openalex.org/W4386041250","https://openalex.org/W4386655647","https://openalex.org/W4387156645","https://openalex.org/W4387164994","https://openalex.org/W4387390444","https://openalex.org/W4387560005","https://openalex.org/W4387614438","https://openalex.org/W4389520455","https://openalex.org/W4390692489","https://openalex.org/W4393147129","https://openalex.org/W6778883912","https://openalex.org/W6846767490","https://openalex.org/W6847753483","https://openalex.org/W6850820320","https://openalex.org/W6854122420","https://openalex.org/W6854475153","https://openalex.org/W6856794988","https://openalex.org/W6856802482","https://openalex.org/W6856858123","https://openalex.org/W6857075306","https://openalex.org/W6857144347","https://openalex.org/W6857305977"],"related_works":["https://openalex.org/W2032233321","https://openalex.org/W3121970507","https://openalex.org/W2110028391","https://openalex.org/W54497855","https://openalex.org/W217960748","https://openalex.org/W3125814499","https://openalex.org/W2090827041","https://openalex.org/W2094012830","https://openalex.org/W187246281","https://openalex.org/W2079194830"],"abstract_inverted_index":{"Large":[0],"Language":[1],"Models":[2],"(LLMs)":[3],"have":[4],"been":[5],"applied":[6],"to":[7,21,64,90,107,163,169,178],"build":[8],"several":[9],"automation":[10],"and":[11,101,111,128,176],"personalized":[12],"question-answering":[13],"prototypes":[14,20],"so":[15],"far.":[16],"However,":[17],"scaling":[18,126],"such":[19,41,173],"robust":[22],"products":[23],"with":[24,119],"minimized":[25],"hallucinations":[26,110],"or":[27],"fake":[28],"responses":[29,103,141],"still":[30],"remains":[31],"an":[32],"open":[33],"challenge,":[34],"especially":[35],"in":[36],"niche":[37],"data-table":[38],"heavy":[39],"domains":[40,172],"as":[42,174],"financial":[43,156],"decision":[44,157],"making.":[45],"In":[46],"this":[47],"work,":[48],"we":[49],"present":[50],"a":[51,66,137],"novel":[52],"Langchain-based":[53],"framework":[54,165],"that":[55,152],"transforms":[56],"data":[57,88,125,162],"tables":[58],"into":[59],"hierarchical":[60],"textual":[61],"\"data":[62],"chunks\"":[63],"enable":[65],"wide":[67],"variety":[68,138],"of":[69,84,139],"actionable":[70],"question":[71],"answering.":[72],"First,":[73],"the":[74,85,98],"user-queries":[75,140],"are":[76,153],"classified":[77],"by":[78,81],"intention":[79,121],"followed":[80],"automated":[82],"retrieval":[83],"most":[86],"relevant":[87],"chunks":[89],"generate":[91],"customized":[92],"LLM":[93],"prompts":[94,100],"per":[95],"query.":[96],"Next,":[97],"custom":[99],"their":[102],"undergo":[104],"multi-metric":[105],"scoring":[106],"assess":[108],"for":[109,136,155],"response":[112],"confidence.":[113],"The":[114,160],"proposed":[115,161],"system":[116],"is":[117],"optimized":[118],"user-query":[120],"classification,":[122],"advanced":[123],"prompting,":[124],"capabilities":[127],"it":[129],"achieves":[130],"over":[131],"$":[132],"90\\%$":[133],"confidence":[134],"scores":[135],"ranging":[142],"from":[143],"{What,":[144],"Where,":[145],"Why,":[146],"How,":[147],"predict,":[148],"trend,":[149],"anomalies,":[150],"exceptions}":[151],"crucial":[154],"making":[158],"applications.":[159],"answers":[164],"can":[166],"be":[167],"extended":[168],"other":[170],"analytical":[171],"sales":[175],"payroll":[177],"ensure":[179],"optimal":[180],"hallucination":[181],"control":[182],"guardrails.":[183]},"counts_by_year":[{"year":2026,"cited_by_count":1},{"year":2025,"cited_by_count":6},{"year":2024,"cited_by_count":4}],"updated_date":"2026-05-05T08:41:31.759640","created_date":"2025-10-10T00:00:00"}
