Year: 2024
Type: article
Abstract: This paper aims at discussing the analysis of health insurance claim through risk classification, fraudulence and cost prediction models. Combined with state-of-art data preprocessing and modelling techniques, insurers can better drive decision, minimize fraud, and better plan for financials. Logistic regression, random forest, gradient boost and models of similar category help in pattern analysis and cost of claim forecasting. They further effectiveness, equity and customer relations for implementing sound insurance that is sustainable. This work therefore emphatically speaks to the Bar on the structural revolution that data modelling has brought on the current health insurance analysis.
DOI: 10.52783/kjact.258
Author: Nandish Shivaprasad
Language: English
Subfield: Health Information Management
Field: Health Professions
Domain: Health Sciences
Open Access status: bronze
OpenAlex ID: W4405829212