Digital financial risk assessment method based on machine learning algorithm
In order to explore more accurate and efficient means of digital financial risk assessment, this paper compares and analyzes the performance of different machine learning algorithms in digital financial risk assessment to find the optimal model configuration, so as to improve the accuracy and timeli...
Gespeichert in:
| Veröffentlicht in: | Procedia computer science Jg. 262; S. 1094 - 1100 |
|---|---|
| Hauptverfasser: | , |
| Format: | Journal Article |
| Sprache: | Englisch |
| Veröffentlicht: |
Elsevier B.V
2025
|
| Schlagworte: | |
| ISSN: | 1877-0509, 1877-0509 |
| Online-Zugang: | Volltext |
| Tags: |
Tag hinzufügen
Keine Tags, Fügen Sie den ersten Tag hinzu!
|
| Abstract | In order to explore more accurate and efficient means of digital financial risk assessment, this paper compares and analyzes the performance of different machine learning algorithms in digital financial risk assessment to find the optimal model configuration, so as to improve the accuracy and timeliness of risk assessment. This paper firstly reviews the relevant literature systematically, and sorts out the current research progress in the field of digital financial risk assessment. Subsequently, the source of data collection and pre-processing steps are introduced in detail to ensure the quality and availability of data. In the feature engineering stage, through the in-depth mining and conversion of the original data, the feature variables that have a key impact on the digital financial risk assessment are extracted, a variety of mainstream algorithms are compared, and the model parameters are optimized by cross-validation and other methods. Experimental results show that the selected algorithm has good predictive performance on specific data sets, and support vector machine (SVM) and random forest algorithm are particularly outstanding on multiple evaluation indicators. |
|---|---|
| AbstractList | In order to explore more accurate and efficient means of digital financial risk assessment, this paper compares and analyzes the performance of different machine learning algorithms in digital financial risk assessment to find the optimal model configuration, so as to improve the accuracy and timeliness of risk assessment. This paper firstly reviews the relevant literature systematically, and sorts out the current research progress in the field of digital financial risk assessment. Subsequently, the source of data collection and pre-processing steps are introduced in detail to ensure the quality and availability of data. In the feature engineering stage, through the in-depth mining and conversion of the original data, the feature variables that have a key impact on the digital financial risk assessment are extracted, a variety of mainstream algorithms are compared, and the model parameters are optimized by cross-validation and other methods. Experimental results show that the selected algorithm has good predictive performance on specific data sets, and support vector machine (SVM) and random forest algorithm are particularly outstanding on multiple evaluation indicators. |
| Author | Jin, Jing Ma, Yusong |
| Author_xml | – sequence: 1 givenname: Jing surname: Jin fullname: Jin, Jing – sequence: 2 givenname: Yusong surname: Ma fullname: Ma, Yusong email: nkmys@126.com |
| BookMark | eNp9kLtOAzEQRS0UJELIF9D4B3axvfGroEDhKUWigdry2rOJw64d2Ssk_p4NoaBimrm3OKPRuUSzmCIgdE1JTQkVN_v6kJMrNSOM14TXdCXO0JwqKSvCiZ79yRdoWcqeTNMopamco8192IbR9rgL0UYXppRD-cC2FChlgDjiAcZd8ri1BTxOEQ_W7UIE3IPNMcQttv025TDuhit03tm-wPJ3L9D748Pb-rnavD69rO82lWOUiwqEALIimlpFWk_ZqgXJ7NS0bzvZUEq576wU3lrNVKMkUUJzzaSUXUuobxaoOd11OZWSoTOHHAabvwwl5ujE7M2PE3N0Ygg3k5OJuj1RML32GSCb4gJEBz5kcKPxKfzLfwMVxW1i |
| Cites_doi | 10.18280/ijsse.140309 |
| ContentType | Journal Article |
| Copyright | 2025 The Author(s) |
| Copyright_xml | – notice: 2025 The Author(s) |
| DBID | 6I. AAFTH AAYXX CITATION |
| DOI | 10.1016/j.procs.2025.05.146 |
| DatabaseName | ScienceDirect Open Access Titles Elsevier:ScienceDirect:Open Access CrossRef |
| DatabaseTitle | CrossRef |
| DatabaseTitleList | |
| DeliveryMethod | fulltext_linktorsrc |
| Discipline | Computer Science |
| EISSN | 1877-0509 |
| EndPage | 1100 |
| ExternalDocumentID | 10_1016_j_procs_2025_05_146 S1877050925019933 |
| GroupedDBID | --K 0R~ 1B1 457 5VS 6I. 71M AAEDT AAEDW AAFTH AAIKJ AALRI AAQFI AAXUO AAYWO ABMAC ABWVN ACGFS ACRPL ACVFH ADBBV ADCNI ADEZE ADNMO ADVLN AEUPX AEXQZ AFPUW AFTJW AGHFR AIGII AITUG AKBMS AKRWK AKYEP ALMA_UNASSIGNED_HOLDINGS AMRAJ E3Z EBS EJD EP3 FDB FNPLU HZ~ IXB KQ8 M41 M~E O-L O9- OK1 P2P ROL SES SSZ ~HD 9DU AAYXX CITATION |
| ID | FETCH-LOGICAL-c2156-e66e04091a80bd124be72a1a89dbf731115dfa76daa9283870869592777fb01d3 |
| ISSN | 1877-0509 |
| IngestDate | Sat Nov 29 07:27:37 EST 2025 Sun Oct 19 01:39:02 EDT 2025 |
| IsDoiOpenAccess | true |
| IsOpenAccess | true |
| IsPeerReviewed | true |
| IsScholarly | true |
| Keywords | Risk assessment Digital finance Machine learning algorithm |
| Language | English |
| License | This is an open access article under the CC BY-NC-ND license. |
| LinkModel | OpenURL |
| MergedId | FETCHMERGED-LOGICAL-c2156-e66e04091a80bd124be72a1a89dbf731115dfa76daa9283870869592777fb01d3 |
| OpenAccessLink | https://dx.doi.org/10.1016/j.procs.2025.05.146 |
| PageCount | 7 |
| ParticipantIDs | crossref_primary_10_1016_j_procs_2025_05_146 elsevier_sciencedirect_doi_10_1016_j_procs_2025_05_146 |
| PublicationCentury | 2000 |
| PublicationDate | 2025 2025-00-00 |
| PublicationDateYYYYMMDD | 2025-01-01 |
| PublicationDate_xml | – year: 2025 text: 2025 |
| PublicationDecade | 2020 |
| PublicationTitle | Procedia computer science |
| PublicationYear | 2025 |
| Publisher | Elsevier B.V |
| Publisher_xml | – name: Elsevier B.V |
| References | Sixin, Bingxin, Hesi (bib9) 2023; 9 Zihui, Pingmiao, Shihan (bib3) 2024; 1 Ben-Yan, Yu-jie (bib6) 2023; 3 Hanbing, Zhixin, Yinan (bib8) 2024; 26 Fei, Lei, Weibing (bib4) 2024; 7 Petrina O, Stadolin M, Kozhina V, et al. Bank Financial Risk Assessment in the Digital Background[J].International Journal of Safety & Security Engineering, 2024, 14(3). Yong (bib2) 2023; 10 Zhang Yuji, Wang Fengxiao. Based on neural network of xinjiang financial risk prediction. Journal of border economy and culture, 2023 (5):31-36. Zijun (bib1) 2023; 3 Da, Yingxue (bib7) 2023; 7 Jiawei (bib10) 2023; 24 Fei (10.1016/j.procs.2025.05.146_bib4) 2024; 7 Jiawei (10.1016/j.procs.2025.05.146_bib10) 2023; 24 10.1016/j.procs.2025.05.146_bib11 10.1016/j.procs.2025.05.146_bib5 Sixin (10.1016/j.procs.2025.05.146_bib9) 2023; 9 Zijun (10.1016/j.procs.2025.05.146_bib1) 2023; 3 Da (10.1016/j.procs.2025.05.146_bib7) 2023; 7 Yong (10.1016/j.procs.2025.05.146_bib2) 2023; 10 Ben-Yan (10.1016/j.procs.2025.05.146_bib6) 2023; 3 Zihui (10.1016/j.procs.2025.05.146_bib3) 2024; 1 Hanbing (10.1016/j.procs.2025.05.146_bib8) 2024; 26 |
| References_xml | – volume: 1 start-page: 131 year: 2024 end-page: 149 ident: bib3 article-title: Research on Risk linkage and Prediction of Stock Market and Bond Market: Based on the frontier perspective of Machine Learning publication-title: Journal of Financial Research – volume: 24 start-page: 24 year: 2023 end-page: 26 ident: bib10 article-title: Research on Construction of risk assessment index system of Supply chain finance in Construction Industry publication-title: Journal of Finance and Economics – volume: 7 start-page: 0134 year: 2024 end-page: 0136 ident: bib4 article-title: Research on payment risk prevention and control of commercial banks based on Machine learning algorithm publication-title: Information Industry Report – reference: Petrina O, Stadolin M, Kozhina V, et al. Bank Financial Risk Assessment in the Digital Background[J].International Journal of Safety & Security Engineering, 2024, 14(3). – volume: 10 start-page: 44 year: 2023 end-page: 61 ident: bib2 article-title: Research on Measurement and Influencing Factors of China’s systemic financial risk based on Machine Learning publication-title: Financial Development Review – volume: 9 start-page: 83 year: 2023 end-page: 94 ident: bib9 article-title: Risk assessment and strategic prevention and control of Internet logistics Finance based on Cloud model in Uka era: from the perspective of small and medium-sized logistics enterprises publication-title: Price Monthly – volume: 7 start-page: 48 year: 2023 end-page: 58 ident: bib7 article-title: Can machine learning methods identify the probability of systemic financial risk in China? publication-title: Financial Market Research – reference: Zhang Yuji, Wang Fengxiao. Based on neural network of xinjiang financial risk prediction. Journal of border economy and culture, 2023 (5):31-36. – volume: 3 start-page: 17 year: 2023 end-page: 27 ident: bib6 article-title: Study on credit breach risk evaluation of Listed companies based on SMOTE LR model publication-title: Developmental Finance Research – volume: 26 start-page: 196 year: 2024 end-page: 206 ident: bib8 article-title: Research on financial risk monitoring and early warning system under the background of digital transformation publication-title: Engineering Science – volume: 3 start-page: 68 year: 2023 end-page: 71 ident: bib1 article-title: Application of Machine Learning Algorithm in financial market risk analysis and prediction publication-title: North and south bridge – volume: 1 start-page: 131 year: 2024 ident: 10.1016/j.procs.2025.05.146_bib3 article-title: Research on Risk linkage and Prediction of Stock Market and Bond Market: Based on the frontier perspective of Machine Learning publication-title: Journal of Financial Research – volume: 7 start-page: 48 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib7 article-title: Can machine learning methods identify the probability of systemic financial risk in China? publication-title: Financial Market Research – volume: 3 start-page: 17 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib6 article-title: Study on credit breach risk evaluation of Listed companies based on SMOTE LR model publication-title: Developmental Finance Research – volume: 26 start-page: 196 issue: 3 year: 2024 ident: 10.1016/j.procs.2025.05.146_bib8 article-title: Research on financial risk monitoring and early warning system under the background of digital transformation publication-title: Engineering Science – ident: 10.1016/j.procs.2025.05.146_bib11 doi: 10.18280/ijsse.140309 – volume: 10 start-page: 44 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib2 article-title: Research on Measurement and Influencing Factors of China’s systemic financial risk based on Machine Learning publication-title: Financial Development Review – ident: 10.1016/j.procs.2025.05.146_bib5 – volume: 7 start-page: 0134 year: 2024 ident: 10.1016/j.procs.2025.05.146_bib4 article-title: Research on payment risk prevention and control of commercial banks based on Machine learning algorithm publication-title: Information Industry Report – volume: 9 start-page: 83 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib9 article-title: Risk assessment and strategic prevention and control of Internet logistics Finance based on Cloud model in Uka era: from the perspective of small and medium-sized logistics enterprises publication-title: Price Monthly – volume: 3 start-page: 68 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib1 article-title: Application of Machine Learning Algorithm in financial market risk analysis and prediction publication-title: North and south bridge – volume: 24 start-page: 24 year: 2023 ident: 10.1016/j.procs.2025.05.146_bib10 article-title: Research on Construction of risk assessment index system of Supply chain finance in Construction Industry publication-title: Journal of Finance and Economics |
| SSID | ssj0000388917 |
| Score | 2.3427696 |
| Snippet | In order to explore more accurate and efficient means of digital financial risk assessment, this paper compares and analyzes the performance of different... |
| SourceID | crossref elsevier |
| SourceType | Index Database Publisher |
| StartPage | 1094 |
| SubjectTerms | Digital finance Machine learning algorithm Risk assessment |
| Title | Digital financial risk assessment method based on machine learning algorithm |
| URI | https://dx.doi.org/10.1016/j.procs.2025.05.146 |
| Volume | 262 |
| hasFullText | 1 |
| inHoldings | 1 |
| isFullTextHit | |
| isPrint | |
| journalDatabaseRights | – providerCode: PRVHPJ databaseName: ROAD: Directory of Open Access Scholarly Resources customDbUrl: eissn: 1877-0509 dateEnd: 99991231 omitProxy: false ssIdentifier: ssj0000388917 issn: 1877-0509 databaseCode: M~E dateStart: 20100101 isFulltext: true titleUrlDefault: https://road.issn.org providerName: ISSN International Centre |
| link | http://cvtisr.summon.serialssolutions.com/2.0.0/link/0/eLvHCXMwtV1LT8JAEN4oevDi24iv7MEbNqGF7naPxkc8IPGAhluz7W4RIsXwMJz87c6-CiohcvDQhm5ggc7HzOww-30IXfoZiWpweDxNiFcnQnpRyIknIRsXmYBDq0S8NGizGbXb7MkSKoy0nADN82g6Ze__amoYA2OrrbMrmLuYFAbgMRgdzmB2OP_J8LfdjhICqWQFl4ZuH-cFBadVja6oACbUnwV93VApnYJEp8LfOoNhd_zan09d9ZYCQJPuQldCEBUbPYsmHCvs5YKhrnNrFz-BpL4zX18wu5CtM4wo9RQ_jIkVC8asBw2sQzU-0K8a2WIbTxUn3UJfbcoGPRUpUkWcHoSKQ9VVJL8xY_-IWEUfoWtR68V6klhNEldDtZ5ZRxsBDZnq8nv8nJXdFPkN0zrMxTdxXFS66-_Xh1mcr8zlIK1dtG0XD_jaGH0Prcl8H-04YQ5s_fQBalgM4AIDWGEAzzCADQawxgAe5NhiADsM4AIDh-j5_q518-BZ2QwvhfwNfmaESHDNzOdRNRGQvyWSBhyumEgyWoPgFoqMUyI4Z5BcgsOOCAtZQCnNkqovakeolA9yeYwwqbPEF5ymKfPraRryUPCIwNP9hMA6QZbRlbs5YAHNjhIvsUkZEXcDYwtRk7jFgIllLzxZ7X1O0Za6MpWyM1QaDyfyHG2mH-PuaHihAfEFak1zJw |
| linkProvider | ISSN International Centre |
| openUrl | ctx_ver=Z39.88-2004&ctx_enc=info%3Aofi%2Fenc%3AUTF-8&rfr_id=info%3Asid%2Fsummon.serialssolutions.com&rft_val_fmt=info%3Aofi%2Ffmt%3Akev%3Amtx%3Ajournal&rft.genre=article&rft.atitle=Digital+financial+risk+assessment+method+based+on+machine+learning+algorithm&rft.jtitle=Procedia+computer+science&rft.au=Jin%2C+Jing&rft.au=Ma%2C+Yusong&rft.date=2025&rft.issn=1877-0509&rft.eissn=1877-0509&rft.volume=262&rft.spage=1094&rft.epage=1100&rft_id=info:doi/10.1016%2Fj.procs.2025.05.146&rft.externalDBID=n%2Fa&rft.externalDocID=10_1016_j_procs_2025_05_146 |
| thumbnail_l | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/lc.gif&issn=1877-0509&client=summon |
| thumbnail_m | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/mc.gif&issn=1877-0509&client=summon |
| thumbnail_s | http://covers-cdn.summon.serialssolutions.com/index.aspx?isbn=/sc.gif&issn=1877-0509&client=summon |