Machine Learning Model for Loan Program Subscription from  Private Lending Institutions

Author(s)	Mr Ronil S Goloran, Mr. Gilbert Alerta, Dr. Fernando T Herrera
Country	Philippines
Abstract	The primary interest of all financial and lending institutions is to determine their market. Determining the probability of loan subscription for every qualified individual remains a challenge. This study develops a machine learning model using logistic regression that classifies loan subscribers from non-subscribers. A total of 300 (150 subscribers and 150 non-subscribers) qualified respondents were surveyed. Subscribing to the machine learning algorithm, 80% (240) and 20% (60) of the respondents were used for training and testing sets, respectively. Input features include: (1) number of existing loans, (2) monthly salary, (3) number of credit cards, (4) awareness to loan program of the company, and (5) required time for loan approval by the company. The data showed that the model was able to correctly classify 87.69% of the training set and 87.50% of the testing set. This result provides empirical evidence that the developed model can be used by lending or financing institutions in future market planning and decision-making.
Keywords	Loan program subscriptions, Private Lending Institutions, Machine Learning Model
Field	Business Administration
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-07-15
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.83516

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