Skip to main content

2024 | OriginalPaper | Buchkapitel

Unveiling the Future: A Review of Financial Fraud Detection Using Artificial Intelligence Techniques

verfasst von : Sankalp Goel, Abha Kiran Rajpoot

Erschienen in: Micro-Electronics and Telecommunication Engineering

Verlag: Springer Nature Singapore

Aktivieren Sie unsere intelligente Suche, um passende Fachinhalte oder Patente zu finden.

search-config
loading …

Abstract

Financial fraud is the illegal use of mobile platforms for transactions when credit card or identity theft is exploited to create fake money. With the spread of smartphones and online transaction services, financial fraud and credit card fraud have become rapidly growing issues. Accurate detection of financial fraud in this context is crucial, as it can result in significant financial losses. Therefore, we conducted a survey of machine learning, deep learning, and data mining methodologies for financial fraud detection. In our study, we evaluated our methodology for detecting fraud and handling vast financial data, contrasting it with artificial neural networks. Our reviewed process encompassed variable selection, sampling, and the utilisation of supervised and unsupervised algorithms. This allowed us to effectively identify financial fraud and process extensive financial datasets.

Sie haben noch keine Lizenz? Dann Informieren Sie sich jetzt über unsere Produkte:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

Literatur
1.
Zurück zum Zitat Liu et al (2018) Machine learning approach in FDS Liu et al (2018) Machine learning approach in FDS
2.
Zurück zum Zitat Zhang S, et al (2019) Deep learning-based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38 Zhang S, et al (2019) Deep learning-based recommender system: a survey and new perspectives. ACM Comput Surv (CSUR) 52(1):1–38
3.
Zurück zum Zitat Dimitrios K (2019) Can artificial intelligence replace whistle-blowers in the business sector. Int J Technol Policy Law 3(2):160–171CrossRef Dimitrios K (2019) Can artificial intelligence replace whistle-blowers in the business sector. Int J Technol Policy Law 3(2):160–171CrossRef
4.
Zurück zum Zitat Panigrahi S, Kundu A, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using dempster Shafer theory and Bayesian learning. Inf Fusion 10(4):354–363CrossRef Panigrahi S, Kundu A, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using dempster Shafer theory and Bayesian learning. Inf Fusion 10(4):354–363CrossRef
5.
Zurück zum Zitat Kundu A, Panigrahi S, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using Dempster–Shafer theory and Bayesian learning. Special Issue Inf Fusion Comput Secur 10(4):354–363 Kundu A, Panigrahi S, Sural S, Majumdar AK (2009) Credit card fraud detection: a fusion approach using Dempster–Shafer theory and Bayesian learning. Special Issue Inf Fusion Comput Secur 10(4):354–363
6.
Zurück zum Zitat Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (1993) Credit card fraud detection using Bayesian and neural networks. In: Interactive image-guided neurosurgery, pp 261–270 Maes S, Tuyls K, Vanschoenwinkel B, Manderick B (1993) Credit card fraud detection using Bayesian and neural networks. In: Interactive image-guided neurosurgery, pp 261–270
7.
Zurück zum Zitat Bentley PJ, Kim J, Jung G-H, Choi J-U (2000) Fuzzy Darwinian detection of credit card fraud. In: 14th Annual fall symposium of the Korean information processing society, 14th October 2000 Bentley PJ, Kim J, Jung G-H, Choi J-U (2000) Fuzzy Darwinian detection of credit card fraud. In: 14th Annual fall symposium of the Korean information processing society, 14th October 2000
8.
Zurück zum Zitat Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn, p 842 Haykin S (1999) Neural networks: a comprehensive foundation, 2nd edn, p 842
9.
Zurück zum Zitat Castelli M, Manzoni L, Popovič A (2016) An artificial intelligence system to predict quality of service in banking organizations. Comput Intell Neurosci Castelli M, Manzoni L, Popovič A (2016) An artificial intelligence system to predict quality of service in banking organizations. Comput Intell Neurosci
10.
Zurück zum Zitat Francois C (2017) Deep learning with Python Francois C (2017) Deep learning with Python
11.
Zurück zum Zitat Brause R, Langsdorf T, Hepp M (1994) Neural data mining for credit card fraud detection. In: International conference on tools with artificial intelligence, pp 621–630 Brause R, Langsdorf T, Hepp M (1994) Neural data mining for credit card fraud detection. In: International conference on tools with artificial intelligence, pp 621–630
12.
Zurück zum Zitat Kundu A, Sural S, Majumdar AK (2006) Two-stage credit card fraud detection using sequence alignment. In: Proceedings of the International conference on information systems security. Lecture notes in computer science, vol 4332, pp 260–275. Springer Verlag Kundu A, Sural S, Majumdar AK (2006) Two-stage credit card fraud detection using sequence alignment. In: Proceedings of the International conference on information systems security. Lecture notes in computer science, vol 4332, pp 260–275. Springer Verlag
13.
Zurück zum Zitat Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11(10):1725–1729CrossRef Ning Z, Cox AJ, Mullikin JC (2001) SSAHA: a fast search method for large DNA databases. Genome Res 11(10):1725–1729CrossRef
14.
Zurück zum Zitat Madden T (2003) The BLAST sequence analysis tool Madden T (2003) The BLAST sequence analysis tool
15.
Zurück zum Zitat Altschul SF, Gish W, Miller W, Myers W, Lipman J (19990) Basic local alignment search tool. J Mol Biol 215:403–410 Altschul SF, Gish W, Miller W, Myers W, Lipman J (19990) Basic local alignment search tool. J Mol Biol 215:403–410
16.
Zurück zum Zitat Sahin Y, Duman E (2011) Detecting credit card fraud by decision trees and support vector machines. In: Proceeding of International multi-conference of engineering and computer statistics, vol 1 Sahin Y, Duman E (2011) Detecting credit card fraud by decision trees and support vector machines. In: Proceeding of International multi-conference of engineering and computer statistics, vol 1
17.
Zurück zum Zitat Dileep MR, Navaneeth AV, Abhishek M (2021) A novel approach for credit card fraud detection using decision tree and random forest algorithms. In: 2021 Third International conference on intelligent communication technologies and virtual mobile networks (ICICV). IEEE Dileep MR, Navaneeth AV, Abhishek M (2021) A novel approach for credit card fraud detection using decision tree and random forest algorithms. In: 2021 Third International conference on intelligent communication technologies and virtual mobile networks (ICICV). IEEE
Metadaten
Titel
Unveiling the Future: A Review of Financial Fraud Detection Using Artificial Intelligence Techniques
verfasst von
Sankalp Goel
Abha Kiran Rajpoot
Copyright-Jahr
2024
Verlag
Springer Nature Singapore
DOI
https://doi.org/10.1007/978-981-99-9562-2_9

Neuer Inhalt