Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024)

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Financial Budget Item Identification Model: Accurately Matching the Budget Items of Reimbursement Claims based on KNN Algorithm

Authors
Junyi Wáng1, Peichun Suo2, *, Weili Kou1, *, Yan Zhang3, Meicai Zhu4
1College of Big Data and Intelligent Engineering, Southwest Forestry University, Kunming, Yunnan, China
2Information Center, Yunnan Medical Health Vocational College, Kunming, Yunnan, China
3The 11th Middle School of Qilin District, Qujing City, China
4Student Affairs Office, Yunnan Medical Health Vocational College, Kunming, Yunnan, China
*Corresponding author. Email: suopeichun_ynzt@163.com
*Corresponding author. Email: kwl@swfu.edu.cn
Corresponding Authors
Peichun Suo, Weili Kou
Available Online 31 August 2024.
DOI
10.2991/978-94-6463-504-1_10How to use a DOI?
Keywords
Budget Items; Grid Search CV; KNN; Decision Tree; GBDT
Abstract

This study aimed to scrutinize the procedural mechanisms underlying the accurate selection of financial budget items in reimbursement forms, to reinforce adherence to predefined expenditure guidelines, enhance the standardization of form completion procedures, and augment work efficiency. The 7,959 reimbursement dataset was collected from a medical vocational college in Yunnan Province of China in 2022 and partitioned into training (80%) and testing (20%). Leveraging Random Forest (RF) complemented by Recursive Feature Elimination (RFE), we selected pertinent attributes. Further, utilizing Grid Search CV, we trained a K-Nearest Neighbors (KNN) predictive model for budget item classification and contrasted its performance with a Decision Tree (DT) and GBDT. Our outcomes illuminated that the prediction accuracy of KNN (81%) is higher than both GBDT (42%) and DT (59%). These insights offer fresh perspectives on the procedural dynamics of financial budgeting, potentially informing strategies for improving financial management practices and form processing efficiency.

Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

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Volume Title
Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024)
Series
Advances in Computer Science Research
Publication Date
31 August 2024
ISBN
978-94-6463-504-1
ISSN
2352-538X
DOI
10.2991/978-94-6463-504-1_10How to use a DOI?
Copyright
© 2024 The Author(s)
Open Access
Open Access This chapter is licensed under the terms of the Creative Commons Attribution-NonCommercial 4.0 International License (http://creativecommons.org/licenses/by-nc/4.0/), which permits any noncommercial use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

Cite this article

risenwbib
TY  - CONF
AU  - Junyi Wáng
AU  - Peichun Suo
AU  - Weili Kou
AU  - Yan Zhang
AU  - Meicai Zhu
PY  - 2024
DA  - 2024/08/31
TI  - Financial Budget Item Identification Model: Accurately Matching the Budget Items of Reimbursement Claims based on KNN Algorithm
BT  - Proceedings of the 2024 3rd International Conference on Information Economy, Data Modelling and Cloud Computing (ICIDC 2024)
PB  - Atlantis Press
SP  - 80
EP  - 86
SN  - 2352-538X
UR  - https://doi.org/10.2991/978-94-6463-504-1_10
DO  - 10.2991/978-94-6463-504-1_10
ID  - Wáng2024
ER  -