WFRF:(Pavlopoulos John)
Sökning: WFRF:(Pavlopoulos John) > Fraud detection wit...
LIBRIS Formathandbok (Information om MARC21)
| Fältnamn | Indikatorer | Metadata |
|---|---|---|
| 000 | 02629naa a2200397 4500 | |
| 001 | oai:DiVA.org:su-221316 | |
| 003 | SwePub | |
| 008 | 230919s2023 | |||||||||||000 ||eng| | |
| 024 | 7 | a https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-2213162 URI |
| 024 | 7 | a https://doi.org/10.1007/s10994-023-06354-52 DOI |
| 040 | a (SwePub)su | |
| 041 | a engb eng | |
| 042 | 9 SwePub | |
| 072 | 7 | a ref2 swepub-contenttype |
| 072 | 7 | a art2 swepub-publicationtype |
| 100 | 1 | a Boulieris, Petrosu Athens University of Economics and Business, Athens, Greece4 aut |
| 245 | 1 0 | a Fraud detection with natural language processing |
| 264 | c 2023 | |
| 264 | 1 | c 2023 |
| 338 | a print2 rdacarrier | |
| 520 | a Automated fraud detection can assist organisations to safeguard user accounts, a task that is very challenging due to the great sparsity of known fraud transactions. Many approaches in the literature focus on credit card fraud and ignore the growing field of online banking. However, there is a lack of publicly available data for both. The lack of publicly available data hinders the progress of the field and limits the investigation of potential solutions. With this work, we: (a) introduce FraudNLP, the first anonymised, publicly available dataset for online fraud detection, (b) benchmark machine and deep learning methods with multiple evaluation measures, (c) argue that online actions do follow rules similar to natural language and hence can be approached successfully by natural language processing methods. | |
| 650 | 7 | a NATURVETENSKAPx Data- och informationsvetenskap0 (SwePub)1022 hsv//swe |
| 650 | 7 | a NATURAL SCIENCESx Computer and Information Sciences0 (SwePub)1022 hsv//eng |
| 653 | a Fraud detection | |
| 653 | a Natural language processing | |
| 653 | a E-banking | |
| 653 | a Feature engineering | |
| 653 | a Varying class imbalance | |
| 700 | 1 | a Pavlopoulos, Johnu Stockholms universitet,Institutionen för data- och systemvetenskap,University of Economics and Business, Athens, Greece4 aut0 (Swepub:su)iopa3492 |
| 700 | 1 | a Xenos, Alexandrosu Athens University of Economics and Business, Athens, Greece4 aut |
| 700 | 1 | a Vassalos, Vasilisu Athens University of Economics and Business, Athens, Greece4 aut |
| 710 | 2 | a Athens University of Economics and Business, Athens, Greeceb Institutionen för data- och systemvetenskap4 org |
| 773 | 0 | t Machine Learningx 0885-6125x 1573-0565 |
| 856 | 4 | u https://doi.org/10.1007/s10994-023-06354-5y Fulltext |
| 856 | 4 8 | u https://urn.kb.se/resolve?urn=urn:nbn:se:su:diva-221316 |
| 856 | 4 8 | u https://doi.org/10.1007/s10994-023-06354-5 |
Hitta mer i SwePub
- Av författaren/redakt...
- Boulieris, Petro ...
- Pavlopoulos, Joh ...
- Xenos, Alexandro ...
- Vassalos, Vasili ...
- Om ämnet
-
- NATURVETENSKAP
- NATURVETENSKAP
- och Data och informa ...
- Artiklar i publikationen
- Machine Learning
- Av lärosätet
- Stockholms universitet
Sök utanför SwePub
- Sök vidare i:
- Google Book Search
- Google Scholar
Kungliga biblioteket hanterar dina personuppgifter i enlighet med EU:s dataskyddsförordning (2018), GDPR. Läs mer om hur det funkar här.
Så här hanterar KB dina uppgifter vid användning av denna tjänst.
- Copyright © LIBRIS - Nationella bibliotekssystem
- LIBRIS.kb.se
