Scott Manski
Current Organizations / Institutes
The organizations/institutes from the scholar's latest article
Research Subjects
Scott Manski has a total of 6 co-authors. Here are the top co-authors by number of shared publications:
- Gee Y. Lee
Department of Statistics and Probability, Department of Mathematics, Michigan State University, East Lansing, MI, USA
2 shared publications - Emilio Xavier Esposito
The Chem21 Group, Inc
1 shared publication - Jiehui Ding
Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
1 shared publication - Kaiwen Wang
Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
1 shared publication - Kristen R. Lidwell
Department of Statistics and Probability, Michigan State University, C413 Wells Hall, 619 Red Cedar Rd, East Lansing, MI 48824, USA
1 shared publication - Tapabrata Maiti
Michigan State University
1 shared publication
Latest Publications
- 22 October 2019
- Published by Cambridge University Press (CUP) in ASTIN Bulletin
- Vol. 50 (1) , 1-24
ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS
In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.
- 22 October 2019
- Published by Cambridge University Press (CUP) in ASTIN Bulletin
- Vol. 50 (1) , 1-24
ACTUARIAL APPLICATIONS OF WORD EMBEDDING MODELS
In insurance analytics, textual descriptions of claims are often discarded, because traditional empirical analyses require numeric descriptor variables. This paper demonstrates how textual data can be easily used in insurance analytics. Using the concept of word similarities, we illustrate how to extract variables from text and incorporate them into claims analyses using standard generalized linear model or generalized additive regression model. This procedure is applied to the Wisconsin Local Government Property Insurance Fund (LGPIF) data, in order to demonstrate how insurance claims management and risk mitigation procedures can be improved. We illustrate two applications. First, we show how the claims classification problem can be solved using textual information. Second, we analyze the relationship between risk metrics and the probability of large losses. We obtain good results for both applications, where short textual descriptions of insurance claims are used for the extraction of features.
- Open Access
Treatment Level and Store Level Analyses of Healthcare Data
The presented research discusses general approaches to analyze and model healthcare data at the treatment level and at the store level. The paper consists of two parts: (1) a general analysis method for store-level product sales of an organization and (2) a treatment-level analysis method of healthcare expenditures. In the first part, our goal is to develop a modeling framework to help understand the factors influencing the sales volume of stores maintained by a healthcare organization. In the second part of the paper, we demonstrate a treatment-level approach to modeling healthcare expenditures. In this part, we aim to improve the operational-level management of a healthcare provider by predicting the total cost of medical services. From this perspective, treatment-level analyses of medical expenditures may help provide a micro-level approach to predicting the total amount of expenditures for a healthcare provider. We present a model for analyzing a specific type of medical data, which may arise commonly in a healthcare provider’s standardized database. We do this by using an extension of the frequency-severity approach to modeling insurance expenditures from the actuarial science literature.
Open AccessTreatment Level and Store Level Analyses of Healthcare Data
The presented research discusses general approaches to analyze and model healthcare data at the treatment level and at the store level. The paper consists of two parts: (1) a general analysis method for store-level product sales of an organization and (2) a treatment-level analysis method of healthcare expenditures. In the first part, our goal is to develop a modeling framework to help understand the factors influencing the sales volume of stores maintained by a healthcare organization. In the second part of the paper, we demonstrate a treatment-level approach to modeling healthcare expenditures. In this part, we aim to improve the operational-level management of a healthcare provider by predicting the total cost of medical services. From this perspective, treatment-level analyses of medical expenditures may help provide a micro-level approach to predicting the total amount of expenditures for a healthcare provider. We present a model for analyzing a specific type of medical data, which may arise commonly in a healthcare provider’s standardized database. We do this by using an extension of the frequency-severity approach to modeling insurance expenditures from the actuarial science literature.