Results 151 to 160 of about 4,440,100 (389)

Pre-trained Online Contrastive Learning for Insurance Fraud Detection

AAAI Conference on Artificial Intelligence
Medical insurance fraud has always been a crucial challenge in the field of healthcare industry. Existing fraud detection models mostly focus on offline learning scenes.
Rui Zhang, Dawei Cheng, Jie Yang, Ouyang Yi, Xian Wu, Yefeng Zheng, Changjun Jiang   +6 more
semanticscholar   +1 more source

Relational Healing: Survivors of Sex Trafficking and Their Experiences of Connection, Disconnection, and Growth

Journal of Counseling &Development, EarlyView.
ABSTRACT In this interpretive phenomenological analysis, we interviewed 10 women to make meaning of their lived experiences of sex trafficking and mental health challenges and how relational dynamics influenced their healing and sense of self. Our analysis revealed four themes: (a) central relational paradox, which described internalized shame and ...
Claudia G. Interiano‐Shiverdecker, Devon E. Romero, David J. Thompson, John J. J. S. Harrichand   +3 more
wiley   +1 more source

Reviewing the role of AI in fraud detection and prevention in financial services

International Journal of Science and Research Archive
This review explores the pivotal role of Artificial Intelligence (AI) in revolutionizing fraud detection and prevention within the realm of financial services.
E. Nwankwo, Olubusola Odeyemi, Noluthando Zamanjomane Mhlongo, Oluwatobi Timothy Soyombo   +3 more
semanticscholar   +1 more source

When Technology Meets Turbulence: The Impact of Digital Transformation and Policy Uncertainty on Audit Opinions

Journal of Corporate Accounting &Finance, EarlyView.
ABSTRACT This study investigates how Corporate Digital Transformation (CDT) influences audit opinions in Chinese A‐share‐listed non‐financial firms from 2011 to 2022, with a focus on the moderating role of Economic Policy Uncertainty (EPU). Results reveal an inverted U‐shaped relationship: moderate levels of CDT enhance audit outcomes through improved ...
He Min, Sultan Sikandar Mirza, Chengming Huang   +2 more
wiley   +1 more source

Sounds Good, Doesn’t Work: The GDPR Principle of Transparency and Data-Driven Welfare Fraud Detection

, 2023
Wes W.P. Damen
openalex   +2 more sources

Fraud Detection using Machine Learning

The winding path of this research into fraud detection using traditional machine learning (ML) led us through landscapes of both impressive strides and thought-provoking hurdles. As we retrace our steps, revisiting key findings and peering into the technology's potential, a nuanced understanding of its effectiveness comes into focus.
Akinbusola Olushola, Joseph Mart
openaire   +1 more source

Fraud, investments and liability regimes in payment platforms [PDF]

In this paper, we discuss how fraud liability regimes impact the price structure that is chosen by a monopolistic payment platform, in a setting where merchants can invest in fraud detection technologies.
Creti, Anna, Verdier, Marianne
core  

Looking Within: Self‐Concept, Cognitive Flexibility, and Emotion Regulation as Intrapersonal Discriminants of Non‐Suicidal Self‐Injury Cessation

Journal of Clinical Psychology, EarlyView.
ABSTRACT Background Given their modifiable nature, understanding the intrapersonal factors involved in the cessation of non‐suicidal self‐injury (NSSI) is needed to inform existing therapeutic approaches and explore novel avenues for supporting individuals with NSSI.
Caitlyn Herrick, Glenn Melvin, David Skvarc, Helen Mildred   +3 more
wiley   +1 more source

Fraud URL Detection

International Journal for Research in Applied Science and Engineering Technology
Fraud detection in digital transactions is a critical concern for financial institutions, e-commerce platforms, and other online service providers. With the increasing sophistication of cybercrimes, traditional fraud detection methods are becoming less effective. This study presents a novel approach to fraud detection using URL link analysis.
openaire   +1 more source

Benford's Law and Fraud Detection. Facts and Legends [PDF]

Is Benford's law a good instrument to detect fraud in reports of statistical and scientific data? For a valid test the probability of "false positives" and "false negatives" has to be low.
Andreas Diekmann, Ben Jann
core