Search Results - subject:"Fraud Detection"

Evaluating Generative AI in enhancing banking services efficiency

Al-Hchemi, Laith Haleem - 2024

repetitive tasks such as loan applications and fraud detection, reducing operational costs, while optimising resource utilisation …

Persistent link: https://www.econbiz.de/10015402805

Evaluating Generative AI in enhancing banking services efficiency

Al-Hchemi, Laith Haleem - In: Economic Forum 14 (2024) 4, pp. 47-54

repetitive tasks such as loan applications and fraud detection, reducing operational costs, while optimising resource utilisation …

Persistent link: https://www.econbiz.de/10015408474

Detecting insurance fraud using supervised and unsupervised machine learning

Debener, Jörn; Heinke, Volker; Kriebel, Johannes - In: Journal of Risk and Insurance 90 (2023) 3, pp. 743-768

supervised learning for insurance fraud detection has long been a research focus, unsupervised learning has rarely been studied … for insurance fraud detection. Accordingly, this study evaluates supervised and unsupervised learning using proprietary …

Persistent link: https://www.econbiz.de/10014504160

Predicting financial statement manipulation in South Africa: A comparison of the Beneish and Dechow models

Marais, Alastair; Vermaak, Claire; Shewell, Patricia - In: Cogent Economics & Finance 11 (2023) 1, pp. 1-33

Recently, South Africa has suffered from several large financial statement frauds. To assist stakeholders in identifying fraud, this study investigated the ability of the Beneish M-score and the Dechow et al. F-score to identify fraud in South Africa. The study also explored similarities in...

Persistent link: https://www.econbiz.de/10015074664

Enhancing financial fraud detection through addressing class imbalance using hybrid SMOTE-GAN techniques

Cheah, Patience Chew Yee; Yang, Yue; Lee, Boon Giin - In: International Journal of Financial Studies : open … 11 (2023) 3, pp. 1-17

The class imbalance problem in finance fraud datasets often leads to biased prediction towards the nonfraud class, resulting in poor performance in the fraud class. This study explores the effects of utilizing the Synthetic Minority Oversampling TEchnique (SMOTE), a Generative Adversarial...

Persistent link: https://www.econbiz.de/10014414370

Fraud-free green finance: using deep learning to preserve the integrity of financial statements for enhanced capital market sustainability

Metawa, Noura; Boujlil, Rhada; Alsunbul, Saad - In: International Journal of Energy Economics and Policy : IJEEP 13 (2023) 6, pp. 610-617

Persistent link: https://www.econbiz.de/10014436022

Detecting insurance fraud using supervised and unsupervised machine learning

Debener, Jörn; Heinke, Volker; Kriebel, Johannes - In: The journal of risk & insurance 90 (2023) 3, pp. 743-768

Persistent link: https://www.econbiz.de/10014364565

A cost-sensitive ensemble deep forest approach for extremely imbalanced credit fraud detection

Zhao, Fang; Li, Gang; Lyu, Yanxia; Ma, Hongdong; Zhu, … - In: Quantitative finance 23 (2023) 10, pp. 1397-1409

Persistent link: https://www.econbiz.de/10014419167

Predicting financial statement manipulation in South Africa : a comparison of the Beneish and Dechow models

Marais, Alastair; Vermaak, Claire; Shewell, Patricia - In: Cogent economics & finance 11 (2023) 1, pp. 1-33

Persistent link: https://www.econbiz.de/10014500717

A supervised machine learning algorithm for detecting and predicting fraud in credit card transactions

Afriyie, Jonathan Kwaku; Tawiah, Kassim; Pels, … - In: Decision analytics journal 6 (2023), pp. 1-12

Fraudsters are now more active in their attacks on credit card transactions than ever before. With the advancement in data science and machine learning, various algorithms have been developed to determine whether a transaction is fraudulent. We study the performance of three different machine...

Persistent link: https://www.econbiz.de/10014505835