An active learning application on loan default prediction: based on forest classifier model

Ronghao Tong

doi:10.1117/12.2671526

28 April 2023 An active learning application on loan default prediction: based on forest classifier model

Ronghao Tong

Proceedings Volume 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022); 126104W (2023) https://doi.org/10.1117/12.2671526
Event: Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 2022, Wuhan, China

Abstract

Under the global pandemic, the application of loans in various forms increases under the influence of economic gloom. Small loan agencies in regions with undeveloped credit systems often neglect background checks because it is costly and time-consuming. Lack of loan approval standards increases the occurrence of defaults. Ideally, a checker program that can make accurate default predictions with a small amount of data contributes to this dilemma. This paper shows a random forest classifier model can achieve 93.2% accuracy incorporating an active learning strategy with only 50 labeled data. Compared to the standard classifier model, the active learning strategy improves the efficiency by 7% with 50 labeled data and increases as much as 14.5% when provided with only 20 labeled data. The source data used in this paper comes from a dataset provided by Univ. AI simulates real-life personal financial records in India.

Citation Download Citation

Ronghao Tong "An active learning application on loan default prediction: based on forest classifier model", Proc. SPIE 12610, Third International Conference on Artificial Intelligence and Computer Engineering (ICAICE 2022), 126104W (28 April 2023); https://doi.org/10.1117/12.2671526

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available