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Seksan Kiatsupaibul. An application of reinforcement learning to credit scoring based on the Logistic Bandit framework. (). King Mongkut's University of Technology North Bangkok. Central Library. : , 2022.
An application of reinforcement learning to credit scoring based on the Logistic Bandit framework
Name: Kantapong Visantavarakul
Organization :
Chulalongkorn University. Department of Statistics
Email :
6480388626@student.chula.ac.th
Name: Seksan Kiatsupaibul
Organization :
Chulalongkorn University. Department of Statistics
Email :
seksan@cbs.chula.ac.th
keyword:
Credit Scoring
ThaSH:
Reinforcement learning
Abstract:
This study applies reinforcement learning to credit scoring by using the logistic bandit framework. The credit
scoring and the credit underwriting are modeled into a single sequential decision problem where the credit
underwriter takes a sequence of actions over an indefinite number of time steps. The traditional credit scoring
approach considers the model construction separately from the underwriting process. This approach is identified as
a greedy algorithm in the reinforcement learning literature, which is commonly believed to be inferior to an efficient
reinforcement learning approach such as Thompson sampling. This is true under the simple setting, i.e., granting
credit to a single borrower per action while the pool of the borrowers is fixed. However, under the more realistic
scenario where these two conditions are relaxed, the greedy approach can outperform Thompson sampling since the
greedy algorithm does not commit too early to an inferior action as it does in the simple setting. Still, the exploration
feature of Thompson sampling is beneficial. When the borrower characteristics are captured by a large number of
features, the exploration mechanism enables Thompson sampling to outperform the greedy algorithm. The results
from the simulation study permit a deeper understanding of the reinforcement learning approaches towards the
logistic bandits, especially in the setting of credit scoring and credit underwriting processes.
King Mongkut's University of Technology North Bangkok. Central Library
Address:
BANGKOK
Email:
library@kmutnb.ac.th
Created:
2022
Modified:
2025-09-15
Issued:
2025-09-15
บทความ/Article
application/pdf
BibliograpyCitation :
In King Mongkut's University of Technology North Bangkok Faculty of Applied Science, Thai Statistical Association (TSA) and Statistics Cooperative Research Network (Statistics CRN). The Proceeding of International Conference on Applied Statistics (ICAS 2022) (pp.95-101). Bangkok : King Mongkut's University of Technology North Bangkok
eng
©copyrights King Mongkut's University of Technology North Bangkok
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0.027077 วินาที
Kantapong Visantavarakul
| Title | Contributor | Type |
|---|---|---|
| An application of reinforcement learning to credit scoring based on the Logistic Bandit framework
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kantapong Visantavarakul;Seksan Kiatsupaibul | บทความ/Article |
Seksan Kiatsupaibul
| Title | Contributor | Type |
|---|---|---|
| An application of reinforcement learning to credit scoring based on the Logistic Bandit framework
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Kantapong Visantavarakul;Seksan Kiatsupaibul | บทความ/Article | |
| An efficiency comparison of distance measures in K-Nearest Neighbors imputation for spatial data
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Prawit Banjong;Seksan Kiatsupaibul | บทความ/Article | |
| Algorithm comparison between Thompson sampling and upper confidence bound for sequential decision problem in the game of Rock-Paper-Scissors
มหาวิทยาลัยเทคโนโลยีพระจอมเกล้าพระนครเหนือ Thanyavuth Akarasomcheep;Seksan Kiatsupaibul | บทความ/Article |