
The role of machine learning in health insurance industry : a case study
Dissertant
Thesis advisor
University
Birzeit University
Faculty
Faculty of Engineering and Technology
Department
Department of Computer Science
University Country
Palestine (West Bank)
Degree
Master
Degree Date
2022
English Abstract
Insurance companies worldwide are exploring how machine learning (ML) can improve customer satisfaction, reduce operational costs and increase profitability.
The greatest opportunity lies in risk prediction and fraud detection, whereas these two topics are the most challenging in the Insurance domain.
The first goal of the study is to predict the risk level in Health Insurance subscribers using machine learning Classification algorithms.
The study included around 10 thousand subscribers who were classified on 3 risk levels (High-risk, Mid-risk, and Low-risk).
The main objective is to predict accurately the risk level and accordingly assist the company in providing an accurate premium rate for new customers.
Some supervised classification algorithms were analyzed and compared using R statistical programming Language; the Random Forest algorithm had the highest accuracy which was around 94%, sensitivity for the High-risk level was around 72%, and 81% for the Mid-risk level.
The second goal of the study is to detect outliers in medical claims to help predict which claims are suspects of fraudulent activities and assist insurance specialists to investigate and discover cases of fraud.
Unsupervised Outlier Detection algorithms assist in discovering abnormal behavior in medical pattern claims and discover cases of fraud.
Three outlier detection techniques were compared, the first using Clustering algorithm (PAM), the second using Densitybased local outliers (LOF), and the third using Isolation Forests (IF).
Outliers with abnormal behavior were detected, some had very high costs compared to the clusters/neighbors they belong to, and others had a very small-time interval between clinical visits which may be suspicious behavior.
Claims audits consume lots of time and are very costly for insurance companies, so having machine learning algorithms detect the suspicious claims that require review will save the company huge amounts of operational costs and will increase work efficiency.
Main Subjects
Information Technology and Computer Science
No. of Pages
93
Table of Contents
Table of contents.
Abstract.
Abstract in Arabic.
Chapter One : Introduction.
Chapter Two : Literature review.
Chapter Three : Methodology.
Chapter Four : Results and discussion.
Chapter Five : Conclusions and recommendations.
References.
American Psychological Association (APA)
Rashmawi, Ruba. (2022). The role of machine learning in health insurance industry : a case study. (Master's theses Theses and Dissertations Master). Birzeit University, Palestine (West Bank)
https://search.emarefa.net/detail/BIM-1429254
Modern Language Association (MLA)
Rashmawi, Ruba. The role of machine learning in health insurance industry : a case study. (Master's theses Theses and Dissertations Master). Birzeit University. (2022).
https://search.emarefa.net/detail/BIM-1429254
American Medical Association (AMA)
Rashmawi, Ruba. (2022). The role of machine learning in health insurance industry : a case study. (Master's theses Theses and Dissertations Master). Birzeit University, Palestine (West Bank)
https://search.emarefa.net/detail/BIM-1429254
Language
English
Data Type
Arab Theses
Record ID
BIM-1429254