| Issue |
MATEC Web Conf.
Volume 392, 2024
International Conference on Multidisciplinary Research and Sustainable Development (ICMED 2024)
|
|
|---|---|---|
| Article Number | 01111 | |
| Number of page(s) | 13 | |
| DOI | https://doi.org/10.1051/matecconf/202439201111 | |
| Published online | 18 March 2024 | |
The Evaluation of Distributed Topic Modeling Paradigms for Detection Of Fraudulent Insurance Claims In Healthcare Forum
1 Department of AI&ML, KG Reddy College of Engineering and Technology, Moinabad, Hyderabad, Telangana-501504
2 Department of Information Technology, GRIET, Hyderabad, Telangana, India
3 Lovely Professional University, Phagwara, Punjab, India.
* Corresponding Author: subbu.kgrcet@gmail.com
Healthcare fraud is the deliberate misrepresentation of the healthcare industry for the purpose of obtaining unjustified financial gain. There are many different types of healthcare fraud, which can influence patients, healthcare professionals, insurers, and government programmes, such as Billing Fraud, Kickbacks and Bribes, Prescription Fraud, False Claims, Provider Licensing Fraud etc...Healthcare insurance fraud is a severe problem that has an impact on everyone's access to affordable healthcare. Topic modelling can play a role in addressing healthcare insurance fraud by assisting in the detection, analysis, and prevention of fraudulent activities. Overall, the public benefits from healthcare insurance fraud detection because it supports equitable, open, and effective healthcare systems.
Key words: Bigdata / Topic modeling / insurance / machine learning / DLDA / DNMF
© The Authors, published by EDP Sciences, 2024
This is an Open Access article distributed under the terms of the Creative Commons Attribution License 4.0, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
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