{"id":"https://openalex.org/W2940311619","doi":"https://doi.org/10.1109/ccis.2018.8691202","title":"Data-driven Risk Assessment for Peer-to-Peer Network Lending Agencies","display_name":"Data-driven Risk Assessment for Peer-to-Peer Network Lending Agencies","publication_year":2018,"publication_date":"2018-11-01","ids":{"openalex":"https://openalex.org/W2940311619","doi":"https://doi.org/10.1109/ccis.2018.8691202","mag":"2940311619"},"language":"en","primary_location":{"id":"doi:10.1109/ccis.2018.8691202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis.2018.8691202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)","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/A5101995167","display_name":"Tianyuan Zhao","orcid":"https://orcid.org/0000-0002-7104-6394"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":true,"raw_author_name":"Tianyuan Zhao","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5100440301","display_name":"Lei Li","orcid":"https://orcid.org/0000-0002-3204-6527"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Lei Li","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"middle","author":{"id":"https://openalex.org/A5101536657","display_name":"Yang Xie","orcid":"https://orcid.org/0000-0002-6075-3026"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yang Xie","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]},{"author_position":"last","author":{"id":"https://openalex.org/A5102244512","display_name":"Yue Lv","orcid":"https://orcid.org/0009-0008-1756-399X"},"institutions":[{"id":"https://openalex.org/I139759216","display_name":"Beijing University of Posts and Telecommunications","ror":"https://ror.org/04w9fbh59","country_code":"CN","type":"education","lineage":["https://openalex.org/I139759216"]}],"countries":["CN"],"is_corresponding":false,"raw_author_name":"Yue Lv","raw_affiliation_strings":["Beijing University of Posts and Telecommunications, Beijing 100876, China"],"affiliations":[{"raw_affiliation_string":"Beijing University of Posts and Telecommunications, Beijing 100876, China","institution_ids":["https://openalex.org/I139759216"]}]}],"institutions":[],"countries_distinct_count":1,"institutions_distinct_count":4,"corresponding_author_ids":["https://openalex.org/A5101995167"],"corresponding_institution_ids":["https://openalex.org/I139759216"],"apc_list":null,"apc_paid":null,"fwci":0.5158,"has_fulltext":false,"cited_by_count":1,"citation_normalized_percentile":{"value":0.76802353,"is_in_top_1_percent":false,"is_in_top_10_percent":false},"cited_by_percentile_year":{"min":89,"max":94},"biblio":{"volume":null,"issue":null,"first_page":"799","last_page":"803"},"is_retracted":false,"is_paratext":false,"is_xpac":false,"primary_topic":{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.5669999718666077,"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"}},"topics":[{"id":"https://openalex.org/T11653","display_name":"Financial Distress and Bankruptcy Prediction","score":0.5669999718666077,"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/T11995","display_name":"FinTech, Crowdfunding, Digital Finance","score":0.2612000107765198,"subfield":{"id":"https://openalex.org/subfields/1404","display_name":"Management Information Systems"},"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/T14413","display_name":"Advanced Technologies in Various Fields","score":0.008200000040233135,"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":0.8552884459495544},{"id":"https://openalex.org/keywords/word2vec","display_name":"Word2vec","score":0.7881336808204651},{"id":"https://openalex.org/keywords/word-embedding","display_name":"Word embedding","score":0.6425038576126099},{"id":"https://openalex.org/keywords/convolutional-neural-network","display_name":"Convolutional neural network","score":0.6102043986320496},{"id":"https://openalex.org/keywords/artificial-intelligence","display_name":"Artificial intelligence","score":0.5744863748550415},{"id":"https://openalex.org/keywords/machine-learning","display_name":"Machine learning","score":0.5140269994735718},{"id":"https://openalex.org/keywords/deep-learning","display_name":"Deep learning","score":0.5113400816917419},{"id":"https://openalex.org/keywords/artificial-neural-network","display_name":"Artificial neural network","score":0.46846267580986023},{"id":"https://openalex.org/keywords/natural-language-processing","display_name":"Natural language processing","score":0.44595056772232056},{"id":"https://openalex.org/keywords/classifier","display_name":"Classifier (UML)","score":0.4226093590259552},{"id":"https://openalex.org/keywords/data-mining","display_name":"Data mining","score":0.339030921459198},{"id":"https://openalex.org/keywords/embedding","display_name":"Embedding","score":0.3257881999015808}],"concepts":[{"id":"https://openalex.org/C41008148","wikidata":"https://www.wikidata.org/wiki/Q21198","display_name":"Computer science","level":0,"score":0.8552884459495544},{"id":"https://openalex.org/C2776461190","wikidata":"https://www.wikidata.org/wiki/Q22673982","display_name":"Word2vec","level":3,"score":0.7881336808204651},{"id":"https://openalex.org/C2777462759","wikidata":"https://www.wikidata.org/wiki/Q18395344","display_name":"Word embedding","level":3,"score":0.6425038576126099},{"id":"https://openalex.org/C81363708","wikidata":"https://www.wikidata.org/wiki/Q17084460","display_name":"Convolutional neural network","level":2,"score":0.6102043986320496},{"id":"https://openalex.org/C154945302","wikidata":"https://www.wikidata.org/wiki/Q11660","display_name":"Artificial intelligence","level":1,"score":0.5744863748550415},{"id":"https://openalex.org/C119857082","wikidata":"https://www.wikidata.org/wiki/Q2539","display_name":"Machine learning","level":1,"score":0.5140269994735718},{"id":"https://openalex.org/C108583219","wikidata":"https://www.wikidata.org/wiki/Q197536","display_name":"Deep learning","level":2,"score":0.5113400816917419},{"id":"https://openalex.org/C50644808","wikidata":"https://www.wikidata.org/wiki/Q192776","display_name":"Artificial neural network","level":2,"score":0.46846267580986023},{"id":"https://openalex.org/C204321447","wikidata":"https://www.wikidata.org/wiki/Q30642","display_name":"Natural language processing","level":1,"score":0.44595056772232056},{"id":"https://openalex.org/C95623464","wikidata":"https://www.wikidata.org/wiki/Q1096149","display_name":"Classifier (UML)","level":2,"score":0.4226093590259552},{"id":"https://openalex.org/C124101348","wikidata":"https://www.wikidata.org/wiki/Q172491","display_name":"Data mining","level":1,"score":0.339030921459198},{"id":"https://openalex.org/C41608201","wikidata":"https://www.wikidata.org/wiki/Q980509","display_name":"Embedding","level":2,"score":0.3257881999015808}],"mesh":[],"locations_count":1,"locations":[{"id":"doi:10.1109/ccis.2018.8691202","is_oa":false,"landing_page_url":"https://doi.org/10.1109/ccis.2018.8691202","pdf_url":null,"source":null,"license":null,"license_id":null,"version":"publishedVersion","is_accepted":true,"is_published":true,"raw_source_name":"2018 5th IEEE International Conference on Cloud Computing and Intelligence Systems (CCIS)","raw_type":"proceedings-article"}],"best_oa_location":null,"sustainable_development_goals":[],"awards":[],"funders":[],"has_content":{"pdf":false,"grobid_xml":false},"content_urls":null,"referenced_works_count":7,"referenced_works":["https://openalex.org/W1832693441","https://openalex.org/W1985721686","https://openalex.org/W2031820816","https://openalex.org/W2087347434","https://openalex.org/W2097117768","https://openalex.org/W2792559751","https://openalex.org/W2963179254"],"related_works":["https://openalex.org/W2946409105","https://openalex.org/W3152932816","https://openalex.org/W2985392712","https://openalex.org/W4388996947","https://openalex.org/W3133567596","https://openalex.org/W2798009317","https://openalex.org/W3203949288","https://openalex.org/W4382201653","https://openalex.org/W3175524270","https://openalex.org/W2998070955"],"abstract_inverted_index":{"With":[0],"the":[1,9,69,147,153,167],"rapid":[2],"development":[3],"of":[4,14,32,52,90,155],"Peer-to-Peer(P2P)":[5],"network":[6,58],"lending":[7,15,59],"in":[8,117],"financial":[10],"field,":[11],"more":[12],"data":[13],"agencies":[16,20,60],"have":[17,22,141],"appeared.":[18],"P2P":[19,44],"also":[21],"problems":[23],"such":[24,74],"as":[25,75],"absconded":[26],"with":[27,126],"ill-gotten":[28],"gains":[29],"and":[30,83,87,103,128,135,160],"out":[31],"business.":[33],"Therefore,":[34],"it":[35],"is":[36],"necessary":[37],"to":[38,85,157,164],"assess":[39],"their":[40],"risks":[41],"based":[42,61],"on":[43,62],"company":[45,91],"data.":[46,66],"This":[47],"paper":[48],"proposes":[49],"a":[50],"framework":[51],"Data-driven":[53],"Risk":[54],"Assessment":[55],"for":[56,110],"P2P(DRAP2P)":[57],"unstructured":[63],"natural":[64,70],"language":[65,71],"First,":[67],"use":[68,152],"processing":[72],"technology,":[73],"word":[76,138],"segmentation,":[77],"keyword,":[78],"LDA":[79],"topic":[80],"model,":[81],"word2vec":[82],"doc2vec,":[84],"process":[86],"extract":[88],"features":[89],"profile":[92],"which":[93],"reflect":[94],"its":[95],"business":[96],"status.":[97],"Then,":[98],"seven":[99],"machine":[100,118],"learning":[101,106,119],"classifiers":[102],"three":[104],"deep":[105],"models":[107],"are":[108],"used":[109],"analysis.":[111],"Since":[112],"keywords":[113,127],"show":[114],"good":[115],"performance":[116],"models,":[120,132],"we":[121,151],"improve":[122],"Convolutional":[123],"Neural":[124],"Network(CNN)":[125],"propose":[129],"two":[130],"CNN+Keyword":[131,136],"namely":[133],"CNN+Keyword(static+BP)":[134,144,159],"(Expand":[137],"embedding).":[139],"Experiments":[140],"shown":[142],"that":[143],"can":[145],"achieve":[146],"best":[148],"performance.":[149,168],"Finally,":[150],"method":[154],"meta-learning":[156],"integrate":[158],"logistic":[161],"regression":[162],"classifier":[163],"further":[165],"strengthen":[166]},"counts_by_year":[{"year":2020,"cited_by_count":1}],"updated_date":"2026-03-03T08:47:05.690250","created_date":"2025-10-10T00:00:00"}
