F21-STAT 441/841 CM 763-Proposal: Difference between revisions
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Ngew, Shwen Lyng | Ngew, Shwen Lyng | ||
Title: | Title: The Application of Classification in Accelerated Underwriting (Insurance) | ||
Description: | Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results. | ||
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility. | |||
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Revision as of 11:14, 8 October 2021
Use this format (Don’t remove Project 0)
Project # 0 Group members:
Last name, First name
Last name, First name
Last name, First name
Last name, First name
Title: Making a String Telephone
Description: We use paper cups to make a string phone and talk with friends while learning about sound waves with this science project. (Explain your project in one or two paragraphs).
Project # 1 Group members:
Feng, Jared
Huang, Xipeng
Xu, Mingwei
Yu, Tingzhou
Title:
Description:
Project # 2 Group members:
Anderson, Eric
Wang, Chengzhi
Zhong, Kai
Zhou, Yi Jing
Title: Application of Neural Networks
Description: Using neural networks to determine content/intent of emails.
Project # 3 Group members:
Chopra, Kanika
Rajcoomar, Yush
Title: Classification
Description: We will be working on the alternate project that the Professor will release on Sunday
Project # 4 Group members:
Zhang, Bowen
Li, Shaozhong
Kerr, Hannah
Wong, Ann gie
Title: Classification
Description: TBD
Project # 5 Group members:
Chin, Jessie Man Wai
Ooi, Yi Lin
Shi, Yaqi
Ngew, Shwen Lyng
Title: The Application of Classification in Accelerated Underwriting (Insurance)
Description: Accelerated Underwriting (AUW), also called “express underwriting,” is a faster and easier process for people with good health condition to obtain life insurance. The traditional underwriting process is often painful for both customers and insurers. From the customer's perspective, they have to complete different types of questionnaires and provide different medical tests involving blood, urine, saliva and other medical results. Underwriters on the other hand have to manually go through every single policy to access the risk of each applicant. AUW allows people, who are deemed “healthy” to forgo medical exams. Since COVID-19, it has become a more concerning topic as traditional underwriting cannot be performed due to the stay-at-home order. However, this imposes a burden on the insurance company to better estimate the risk associated with less testing results.
This is where data science kicks in. With different classification methods, we can address the underwriting process’ five pain points: labor, speed, efficiency, pricing and mortality. This allows us to better estimate the risk and classify the clients for whether they are eligible for accelerated underwriting. For the final project, we use the data from one of the leading US insurers to analyze how we can classify our clients for AUW using the method of classification. We will be using factors such as health data, medical history, family history as well as insurance history to determine the eligibility.
Project # 6 Group members:
Wang, Carolyn
Cyrenne, Ethan
Nguyen, Dieu Hoa
Sin, Mary Jane
Title: TBD
Description: TBD
Project # 7 Group members:
Bhattacharya, Vaibhav
Chatoor, Amanda
Prathap Das, Sutej
Title: PetFinder.my - Pawpularity Contest [1]
Description: In this competition, we will analyze raw images and metadata to predict the “Pawpularity” of pet photos. We'll train and test our model on PetFinder.my's thousands of pet profiles.
Project # 8 Group members:
Xu, Siming
Yan, Xin
Duan, Yishu
Di, Xibei
Title: TBD
Description: TBD
Project # 9 Group members:
Loke, Chun Waan
Chong, Peter
Osmond, Clarice
Li, Zhilong
Title: TBD
Description: TBD
Project # 10 Group members:
O'Farrell, Ethan
D'Astous, Justin
Hamed, Waqas
Vladusic, Stefan
Title: Pawpularity (Kaggle)
Description: Predicting the popularity of animal photos based on photo metadata
Project # 11 Group members:
JunBin, Pan
Title: TBD
Description: TBD
Project # 12 Group members:
Kar Lok, Ng
Muhan (Iris), Li
Wu, Mingze
Title: NFL Health & Safety - Helmet Assignment competition (Kaggle Competition)
Description: Assigning players to the helmet in a given footage of head collision in football play.
Project # 13 Group members:
Livochka, Anastasiia
Wong, Cassandra
Evans, David
Yalsavar, Maryam
Title: TBD
Description: TBD
Project # 14 Group Members:
Syamala, Aavinash Reddy
Zhu, Jigang
Title: TBD
Description: TBD
Project # 15 Group Members:
Zeng, Mingde
Lin, Xiaoyu
Fan, Joshua
Rao, Chen Min
Title: TBD
Description: TBD
Project # 16 Group Members:
Huang, Yuying
Anugu, Ankitha
Dave, Meet Hemang
Chen, Yushan
Title: TBD
Description: TBD
Project # 17 Group Members:
Wang, Lingshan
Liu, Ziyi
Zheng, Hanxi
Li, Yifan
Title: Implement and Improve CNN in Multi-Class Text Classification
Description: We are going to apply Convolutional Neural Network (CNN) to classify real-world data (application to build an efficient insurance contract classifier) and improve CNN algorithm-wise in the context of text classification, being supported with real-world data set. With the implementation of CNN, it allows us to further analyze the efficiency and practicality of the algorithm. The dataset is composed of insurance contracts containing client and policy information. We will implement a multi-class classification to break down the information contained in each insurance contract into some pre-determined subcategories (eg, short-term renewable/long-term non-renewable). We will attempt to process the complicated data into several data types(e.g. JSON, pandas data frames, etc.) and choose the most efficient raw data processing logic based on runtime and algorithm optimization.