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Pregled bibliografske jedinice broj: 916126

Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning


Klepac, Goran
Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning // Cognitive Computing for Big Data Systems Over IoT / Sangaiah, A ; Thangavelu, A ; Meenakshi Sundaram, V (ur.).
Cham: Springer International Publishing, 2018. str. 79-95 doi:10.1007/978-3-319-70688-7_4


Naslov
Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning

Autori
Klepac, Goran

Vrsta, podvrsta i kategorija rada
Poglavlja u knjigama, znanstveni

Knjiga
Cognitive Computing for Big Data Systems Over IoT

Urednik/ci
Sangaiah, A ; Thangavelu, A ; Meenakshi Sundaram, V

Izdavač
Springer International Publishing

Grad
Cham

Godina
2018

Raspon stranica
79-95

ISBN
978-3-319-70687-0

ISSN
2367-4512

Ključne riječi
Benford’s law Fuzzy expert system Cognitive data science Fraud detection Machine learning

Sažetak
Developing fraud detection models always has been challenging area. Low frequency of fraudulent cases within data, indications instead of certainty contribute to very challenging area for data science method applying. Traditional approach of predictive modelling became insufficient, because relaying on few variables as a base of the fraud model are very fragile concept. Reason for that is fact that we are talking about portfolio with low cases of events, and from the other hand it is unrealistic to lean on few variables articulated through logistic regression, neural network or similar method that will be able to detect sophisticated try of fraudulent activities. Chapter gives proposal how to use data science in such situations where there are no solid bases but only potential suspicious regarding fraudulent activities. For those purposes Benford’s law in combination with other data science methods and fuzzy logic will be used on sample data set, and will be shown potentials of proposed methodology for fraud detection purposes. Chapter shows case study in domain of finance on public data, where proposed methodology will be illustrated an efficient methodology which can be usable for fraud detection purposes.

Izvorni jezik
Engleski

Znanstvena područja
Informacijske i komunikacijske znanosti



POVEZANOST RADA


Profili:

Avatar Url Goran Klepac (autor)

Citiraj ovu publikaciju

Klepac, Goran
Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning // Cognitive Computing for Big Data Systems Over IoT / Sangaiah, A ; Thangavelu, A ; Meenakshi Sundaram, V (ur.).
Cham: Springer International Publishing, 2018. str. 79-95 doi:10.1007/978-3-319-70688-7_4
Klepac, G. (2018) Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning. U: Sangaiah, A., Thangavelu, A. & Meenakshi Sundaram, V. (ur.) Cognitive Computing for Big Data Systems Over IoT. Cham, Springer International Publishing, str. 79-95 doi:10.1007/978-3-319-70688-7_4.
@inbook{inbook, author = {Klepac, G.}, year = {2018}, pages = {79-95}, DOI = {10.1007/978-3-319-70688-7\_4}, keywords = {Benford’s law Fuzzy expert system Cognitive data science Fraud detection Machine learning}, doi = {10.1007/978-3-319-70688-7\_4}, isbn = {978-3-319-70687-0}, issn = {2367-4512}, title = {Cognitive Data Science Automatic Fraud Detection Solution, Based on Benford’S Law, Fuzzy Logic with Elements of Machine Learning}, keyword = {Benford’s law Fuzzy expert system Cognitive data science Fraud detection Machine learning}, publisher = {Springer International Publishing}, publisherplace = {Cham} }

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