Data driven recommendation of personal insurance policies
With the advancement of technology, Singapore remains as one of the most digitally competitive country in the world. With such progress as a technology hub, this had also led to the increase in the median gross monthly salary of fresh graduates. The shifting norms of neglecting insurances by most in...
Saved in:
Main Author: | |
---|---|
Other Authors: | |
Format: | Final Year Project |
Language: | English |
Published: |
Nanyang Technological University
2023
|
Subjects: | |
Online Access: | https://hdl.handle.net/10356/166034 |
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Institution: | Nanyang Technological University |
Language: | English |
id |
sg-ntu-dr.10356-166034 |
---|---|
record_format |
dspace |
spelling |
sg-ntu-dr.10356-1660342023-04-21T15:39:33Z Data driven recommendation of personal insurance policies Heng, Sabrina Chor Chen Sourav S Bhowmick School of Computer Science and Engineering ASSourav@ntu.edu.sg Engineering::Computer science and engineering With the advancement of technology, Singapore remains as one of the most digitally competitive country in the world. With such progress as a technology hub, this had also led to the increase in the median gross monthly salary of fresh graduates. The shifting norms of neglecting insurances by most in the past has also changed to the younger ones seeing the importance of them. However, the average sales process of purchasing a policy that includes prospecting and advisory takes about one to two hours with prospecting taking about thirty minutes. The key aim of this project is to shorten the whole advisory process, mainly prospecting, from thirty minutes to a five-minute session while still ensuring that the needs and wants of the person are met. In doing so, this report will take one through the design, development, and data-analysis of an insurance Webform where users can answer a short series of questions, and a recommendation of insurance policies will be generated. The recommendation will be based on two things – inputs of users, and the usage of past users’ insurance data – to generate the policies recommended. To do so, a comprehensive study must first be done on the current solutions of the finance industry. We will then look at how this recommendation system helps bridge the current gap and provide a better efficient process. We will then look into the criteria of what makes an ideal recommendation system based on different individuals and what are some of the important questions to include in the webform before generating a personalised recommendation. The recommendation will be split into two parts, the first we will be going through how user inputs affect recommendations, and the second is based on past trends data, which will affect the recommendations to the first part. Bachelor of Engineering (Computer Science) 2023-04-19T07:44:25Z 2023-04-19T07:44:25Z 2023 Final Year Project (FYP) Heng, S. C. C. (2023). Data driven recommendation of personal insurance policies. Final Year Project (FYP), Nanyang Technological University, Singapore. https://hdl.handle.net/10356/166034 https://hdl.handle.net/10356/166034 en SCSE22-0326 application/pdf Nanyang Technological University |
institution |
Nanyang Technological University |
building |
NTU Library |
continent |
Asia |
country |
Singapore Singapore |
content_provider |
NTU Library |
collection |
DR-NTU |
language |
English |
topic |
Engineering::Computer science and engineering |
spellingShingle |
Engineering::Computer science and engineering Heng, Sabrina Chor Chen Data driven recommendation of personal insurance policies |
description |
With the advancement of technology, Singapore remains as one of the most digitally competitive country in the world. With such progress as a technology hub, this had also led to the increase in the median gross monthly salary of fresh graduates. The shifting norms of neglecting insurances by most in the past has also changed to the younger ones seeing the importance of them. However, the average sales process of purchasing a policy that includes prospecting and advisory takes about one to two hours with prospecting taking about thirty minutes.
The key aim of this project is to shorten the whole advisory process, mainly prospecting, from thirty minutes to a five-minute session while still ensuring that the needs and wants of the person are met. In doing so, this report will take one through the design, development, and data-analysis of an insurance Webform where users can answer a short series of questions, and a recommendation of insurance policies will be generated. The recommendation will be based on two things – inputs of users, and the usage of past users’ insurance data – to generate the policies recommended.
To do so, a comprehensive study must first be done on the current solutions of the finance industry. We will then look at how this recommendation system helps bridge the current gap and provide a better efficient process. We will then look into the criteria of what makes an ideal recommendation system based on different individuals and what are some of the important questions to include in the webform before generating a personalised recommendation.
The recommendation will be split into two parts, the first we will be going through how user inputs affect recommendations, and the second is based on past trends data, which will affect the recommendations to the first part. |
author2 |
Sourav S Bhowmick |
author_facet |
Sourav S Bhowmick Heng, Sabrina Chor Chen |
format |
Final Year Project |
author |
Heng, Sabrina Chor Chen |
author_sort |
Heng, Sabrina Chor Chen |
title |
Data driven recommendation of personal insurance policies |
title_short |
Data driven recommendation of personal insurance policies |
title_full |
Data driven recommendation of personal insurance policies |
title_fullStr |
Data driven recommendation of personal insurance policies |
title_full_unstemmed |
Data driven recommendation of personal insurance policies |
title_sort |
data driven recommendation of personal insurance policies |
publisher |
Nanyang Technological University |
publishDate |
2023 |
url |
https://hdl.handle.net/10356/166034 |
_version_ |
1764208156482732032 |