Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review

Benedek, Botond; Ciumaș, Cristina; Nagy, Bálint Zsolt

doi:10.1108/jfrc-11-2021-0102

Cited by 13 publications

(4 citation statements)

References 51 publications

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“…Companies like IBM and Reuters have been using natural language processing in machine learning systems to gain insights from large amount of unstructured data. AI is also being used in detecting frauds, rule compliance and ensuring security of data in various industries (Baek et al, 2022;Farhang Ghahfarokhi et al, 2021;Roszkowska, 2021;Benedek et al, 2022;Omar et al, 2017;Khattri and Singh, 2018).Various customer services also use AI to cater to the various demands of the customers. The transport industry is extensively using big data-based AI systems to understand and react to meet customer expectations (Lukka and Lukka, 1988;Dong et al, 2021;P ribyl, 2006).…”

Section: Exploring the Benefits Of Big Data Combined With The Artific...mentioning

confidence: 99%

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Arora

Sharma

2022

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Purpose The purpose of this paper is to see how critical and vital artificial intelligence (AI) and big data are in today’s world. Besides this, this paper also seeks to explore qualitative and theoretical perspectives to underscore the importance of AI and big data applications in multi-sectoral scenarios of businesses across the world. Moreover, this paper also aims at working out the scope of ontological communicative perspectives based on AI alongside emphasizing their relevance in business organizations that need to survive and sustain with a view to achieve their strategic goals. Design/methodology/approach This paper attempts to explore the qualitative perspectives to build a direction for strategic management via addressing the following research questions concerned with assessing the scope of ontological communicative perspectives in AI relevant to business organizations; exploring benefits of big data combined with AI in modern businesses; and underscoring the importance of AI and big data applications in multi-sectoral scenarios of businesses in today’s world. Employing bibliometric analysis along with NVivo software to do sentiment analysis, this paper attempts to develop an understanding of what happens when AI and big data are combined in businesses. Findings AI and big data have tremendous bearing on modern businesses. Because big data comprises enormous information of diverse sorts, AI-assisted machines, tools and devices help modern businesses process it quickly, efficiently and meaningfully. Therefore, business leaders and entrepreneurs need to focus heavily on ontological and communicative perspectives to deal with diverse range of challenges and problems particularly in the context of recent crises caused by COVID-19 pandemic. Research limitations/implications There is hardly any arena of human activity wherein AI and big data are not relevant. The implication of this paper is that of combining both well so that we may find answers to the difficult and challenging multi-sectoral scenarios concerning not just businesses but life at large. Moreover, automated tools based on AI such as natural language processing and speech to text also facilitate meaningful communication at various levels not just in business organizations but other fields of human activities as well. Social implications This paper has layered social implications, as it conceptually works out as to how strategically we may combine AI and big data to benefit modern business scenarios dealing with service providers, manufacturers, entrepreneurs, business leaders, customers and consumers. All the stakeholders are socio-culturally and contextually rooted/situated, and that is how this study becomes socially relevant. Originality/value This paper is an original piece of research and has been envisioned in view of the challenging business scenarios across the world today. This paper underscores the importance of strategically combining AI and big data, as they have enormous bearing on modern businesses. The insights arrived at in this paper have implications for business leaders and entrepreneurs across the globe who could focus more on ontological and communicative perspectives of AI combined with Big Data to deal with diverse range of challenges and problems that modern businesses have been facing particularly in recent times.

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Section: Exploring the Benefits Of Big Data Combined With The Artific...mentioning

confidence: 99%

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Arora

Sharma

2022

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“…Significant research exists related to the general insurance fraud detection in the past that focuses on data mining and machine learning techniques [16]. Researchers have mostly focused on one of the stakeholders of insurance triangle , more frequently, on the frauds done by patients or by the hospitals.…”

Section: Related Workmentioning

confidence: 99%

Healthcare insurance fraud detection using data mining

Hamid,

Khalique,

Mahmood

et al. 2024

BMC Med Inform Decis Mak

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Background Healthcare programs and insurance initiatives play a crucial role in ensuring that people have access to medical care. There are many benefits of healthcare insurance programs but fraud in healthcare continues to be a significant challenge in the insurance industry. Healthcare insurance fraud detection faces challenges from evolving and sophisticated fraud schemes that adapt to detection methods. Analyzing extensive healthcare data is hindered by complexity, data quality issues, and the need for real-time detection, while privacy concerns and false positives pose additional hurdles. The lack of standardization in coding and limited resources further complicate efforts to address fraudulent activities effectively. Methodolgy In this study, a fraud detection methodology is presented that utilizes association rule mining augmented with unsupervised learning techniques to detect healthcare insurance fraud. Dataset from the Centres for Medicare and Medicaid Services (CMS) 2008-2010 DE-SynPUF is used for analysis. The proposed methodology works in two stages. First, association rule mining is used to extract frequent rules from the transactions based on patient, service and service provider features. Second, the extracted rules are passed to unsupervised classifiers, such as IF, CBLOF, ECOD, and OCSVM, to identify fraudulent activity. Results Descriptive analysis shows patterns and trends in the data revealing interesting relationship among diagnosis codes, procedure codes and the physicians. The baseline anomaly detection algorithms generated results in 902.24 seconds. Another experiment retrieved frequent rules using association rule mining with apriori algorithm combined with unsupervised techniques in 868.18 seconds. The silhouette scoring method calculated the efficacy of four different anomaly detection techniques showing CBLOF with highest score of 0.114 followed by isolation forest with the score of 0.103. The ECOD and OCSVM techniques have lower scores of 0.063 and 0.060, respectively. Conclusion The proposed methodology enhances healthcare insurance fraud detection by using association rule mining for pattern discovery and unsupervised classifiers for effective anomaly detection.

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“…Based on the dataset and list of damaged auto parts obtained through the preprocessing [9], we further filtered the auto part list. Subsequently, every auto part was connected with all the other parts, resulting in a fully connected dataset T containing all the auto parts with their corresponding relationships.…”

Section: Construction Of Fully Connected Relationships Among Partsmentioning

confidence: 99%

A Method for Anti-Leakage in Car Insurance Based on the Co-Occurrence Relationship Between Auto Parts

Jiang,

Yang,

Xiong

et al. 2023

Frontiers in Artificial Intelligence and Applications

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Car insurance fraud is a high-risk area in insurance and it accounts for as much as 80% of all insurance fraud, with a significant portion of it stemming from the risk of parts leakage. Current anti-leakage techniques in auto insurance mainly rely on the analysis of individual parts data such as vehicle accident records and parts damage lists, which neglects the consideration of the correlation among parts and leads to difficulty in identifying leaked parts effectively. This study proposes a method based on the co-occurrence relationship and density clustering of auto parts to detect parts leakage. In this research, an undisclosed dataset on car insurance fraud was utilized to conduct experiments, and the detection results of parts leakage were obtained. This method takes into account the correlation among auto parts, and possesses higher accuracy and practicality.

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Automobile insurance fraud detection in the age of big data – a systematic and comprehensive literature review

Cited by 13 publications

References 51 publications

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Artificial intelligence and big data: ontological and communicative perspectives in multi-sectoral scenarios of modern businesses

Healthcare insurance fraud detection using data mining

A Method for Anti-Leakage in Car Insurance Based on the Co-Occurrence Relationship Between Auto Parts

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