Results 161 to 170 of about 216,890 (268)

Research on quantitative evaluation of medical insurance fraud supervision policy based on 'Antecedents-Process-Outcomes' framework. [PDF]

PLoS One
Zhang Z, Ding S, Yang Z, Hu H.
europepmc   +1 more source

Insurance fraud prediction with machine learning

Insurance Fraud is a problem which has received little attention in the machine learning literature. The two common fraud types encountered in insurance are Claims and Underwriting fraud (Viaene, 2004). One specific feature shared by both types is that the fraud status in non-self- revealing, which challenges the completeness and existence of a label ...
openaire   +3 more sources

When Business Breaks the Rules: The Value of a Criminology‐Informed “Organizational” Perspective for the Regulation of White‐Collar and Corporate Crimes

Regulation &Governance, EarlyView.
ABSTRACT This article argues that if the aspiration is to enhance regulatory and governance responses to white‐collar and corporate crimes, consideration of the organization of these offending behaviors must be central to the scholarly, practice, and policy discussion.
Nicholas Lord, Michael Levi
wiley   +1 more source

Representation, medical examination, and the effectiveness of underwriting: Evidence from a life and health insurance company

Risk Management and Insurance Review, EarlyView.
Abstract This study examines whether underwriting methods—representation, medical examination, extra premiums, and coverage—effectively mitigate adverse selection using data from an insurance company. Regarding representation, we focus on statements disclosing pre‐existing medical conditions.
Chia‐Ling Ho, Gene Lai, Chen‐Hsu Liao, Zifen Zeng   +3 more
wiley   +1 more source

Perception of Enrollee Health Insurance Fraud among Healthcare Workers at a Tertiary Hospital in Kaduna State, North-western Nigeria. [PDF]

Niger Med J
Kasim HA, Suleiman AG, Saulawa FA, Usman S, Aliyu M, Aminu NS.   +5 more
europepmc   +1 more source

A Machine Learning and Explainable Artificial Intelligence approach for Insurance Fraud Classification

Inteligencia Artificial
This study addresses the critical issue of fraud in the vehicle insurance market by introducing a comprehensive framework that employs advanced detection models, including explainable artificial intelligence (XAI) and heterogeneous classifiers to ...
Diana Olivia, Zaid Khan, Sucharitha Shetty   +2 more
doaj  

Moral Assumptions in Causal Thought: Poverty and Perversity

Sociological Forum, EarlyView.
ABSTRACT Causal attributions, framings, and ideas shape moral judgments. Sociologists have long highlighted these causality‐to‐morality processes, showing how causality underpins blame and moral responsibility. The reverse process of morality‐to‐causality, where moral assumptions influence causal attributions, has been studied less.
Lukas Posselt
wiley   +1 more source

Understanding Gaps in the Provision of Long‐Term Care for Older People Across Europe

Social Policy &Administration, EarlyView.
ABSTRACT Since the 1990s, European welfare states have expanded long‐term care (LTC) support for older people, yet gaps in social security and unmet needs persist. This study identifies key gaps in LTC provision and examines how differences in policy design contribute to them.
Christopher Grages
wiley   +1 more source

Optimizing Human Resource Conditions for 20‐Year Initial Public Offering (IPO) Survival

Personnel Psychology, Volume 79, Issue 2, Page 107-134, Summer 2026.
ABSTRACT The entrepreneurship literature emphasizes the importance of imprints founders leave on companies; those imprints can change at transformational events such as the initial public offering (IPO). Prior research has found that objective measures (such as structure or compensation systems) can imprint and predict survival post IPO.
Theresa M. Welbourne, Miranda J. Welbourne Eleazar, Joshua V. White   +2 more
wiley   +1 more source

Explainable Artificial Intelligence Through the Lens of Bibliometric Citation Analysis

Applied Stochastic Models in Business and Industry, Volume 42, Issue 3, May/June 2026.
ABSTRACT This study provides a comprehensive bibliometric analysis of the development of Explainable Artificial Intelligence (XAI) research from 1993 to 2024. The objective is to explore key contributors, thematic trends, and the evolution of methodologies within the field.
Mariateresa Russo, Domenico Vistocco
wiley   +1 more source