{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,5,2]],"date-time":"2026-05-02T07:08:06Z","timestamp":1777705686661,"version":"3.51.4"},"reference-count":31,"publisher":"SAGE Publications","issue":"5","content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["IFS"],"published-print":{"date-parts":[[2023,5,4]]},"abstract":"<jats:p>The fraud problem has drastically increased with the rapid growth of online lending. Since loan applications, approvals and disbursements are operated online, deceptive borrowers are prone to conceal or falsify information to maliciously obtain loans, while lenders have difficulty in identifying fraud without direct contacts and lack binding force on customers\u2019 loan performance, which results in the frequent occurrence of fraud events. Therefore, it is significant for financial institutions to apply valuable data and competitive technologies for fraud detection to reduce financial losses from loan scams. This paper combines the advantages of statistical methods and ensemble learning algorithms to design the grouped trees and weighted ensemble algorithm (GTWE), and establishes fraud prediction models for online loans based on mobile application usage behaviors(App behaviors) by logistic regression, extreme gradient boosting (XGBoost), long short-term memory (LSTM) and the GTWE algorithm, respectively. The experimental results show that the fraud prediction model based on the GTWE algorithm achieves outstanding classification effect and stability with satisfactory interpretability. Meanwhile, the fraud probability of customers detected by the fraud prediction model is as high as 84.19%, which indicates that App behaviors have a considerable impact on predicting fraud in online loan application.<\/jats:p>","DOI":"10.3233\/jifs-222405","type":"journal-article","created":{"date-parts":[[2023,2,3]],"date-time":"2023-02-03T11:13:20Z","timestamp":1675422800000},"page":"7181-7194","source":"Crossref","is-referenced-by-count":0,"title":["An ensemble fraud detection approach for online loans based on application usage patterns"],"prefix":"10.1177","volume":"44","author":[{"given":"Meiling","family":"Xu","sequence":"first","affiliation":[{"name":"School of Mathematics, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Yongqiang","family":"Fu","sequence":"additional","affiliation":[{"name":"School of Mathematics, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Boping","family":"Tian","sequence":"additional","affiliation":[{"name":"School of Mathematics, Harbin Institute of Technology, Harbin, China"}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"179","reference":[{"key":"10.3233\/JIFS-222405_ref2","first-page":"9777","article-title":"Deep learninganti-fraud model for internet loan: where we are going","volume":"9","author":"Fang","year":"2021","journal-title":"IEEEAccess"},{"issue":"4","key":"10.3233\/JIFS-222405_ref3","doi-asserted-by":"crossref","first-page":"347","DOI":"10.3103\/S0146411622040046","article-title":"A new improved method for online creditanti-fraud","volume":"56","author":"Kang","year":"2022","journal-title":"Automatic Control and Computer Sciences"},{"issue":"4","key":"10.3233\/JIFS-222405_ref4","doi-asserted-by":"crossref","first-page":"2847","DOI":"10.1007\/s13369-019-04190-1","article-title":"Fingerprint spoofing detection to improvecustomer security in mobile financial applications using deeplearning","volume":"45","author":"Arora","year":"2020","journal-title":"Arabian Journal for Science and Engineering"},{"issue":"2","key":"10.3233\/JIFS-222405_ref5","doi-asserted-by":"crossref","first-page":"137","DOI":"10.1515\/fcds-2017-0006","article-title":"Evaluation of face detectionalgorithms for the bank client identity verification","volume":"42","author":"Szczodrak","year":"2017","journal-title":"Foundations of Computing and Decision Sciences"},{"issue":"26","key":"10.3233\/JIFS-222405_ref6","doi-asserted-by":"crossref","first-page":"38113","DOI":"10.1007\/s11042-022-12782-7","article-title":"DeepSignature: fine-tuned transferlearning based signature verification system","volume":"81","author":"Naz","year":"2022","journal-title":"Multimedia Toolsand Applications]"},{"key":"10.3233\/JIFS-222405_ref7","first-page":"1","article-title":"data acquired with experimental multimodal biometricsystem installed in bank branches","volume":"52","author":"Szczuko","year":"2019","journal-title":"Journal of IntelligentInformation Systems"},{"issue":"2","key":"10.3233\/JIFS-222405_ref8","doi-asserted-by":"crossref","first-page":"595","DOI":"10.1016\/j.ejor.2008.02.015","article-title":"On the communalanalysis suspicion scoring for identity crime in streaming creditapplications","volume":"195","author":"Phua","year":"2009","journal-title":"European Journal of Operational Research"},{"key":"10.3233\/JIFS-222405_ref10","doi-asserted-by":"crossref","unstructured":"Wang J. , Guo Y. , Wen X. , Wang Z. , Li Z. and Tang M. , Improvinggraph-based label propagation algorithm with group partition forfraud detection, Applied Intelligence 50(10) (2020).","DOI":"10.1007\/s10489-020-01724-1"},{"key":"10.3233\/JIFS-222405_ref11","doi-asserted-by":"crossref","unstructured":"Ehatisham-ul-Haq M. , Azam M.A. , Loo J. , Shuang K. and Islam S. , U.Naeem and Y. 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