ItemConference Paper

Financial Behaviour Analysis for Payment Bank Adoption Using Random Forest and PCA: An Indian Perspective

Title
Financial Behaviour Analysis for Payment Bank Adoption Using Random Forest and PCA: An Indian Perspective
Creator
Ramana, Nagella Venkata; Kumar, Mukesh; Vidyashree, D.V.; Chand Basha, S.; Pandey, Vivekanand; Kaliappan, S.
Description
The gradual acceptance of payment banks in India constitutes an important challenge to equitable financial development, especially for underbanked rural communities. The proposed study handles the difficulty by examining financial behaviour through a hybrid machine learning methodology that integrates Principal Component Analysis (PCA) for dimensionality reduction with a Random Forest classifier for predictive modelling. The study utilises datasets obtained from Kaggle that reflect demographic, behavioural, and digital engagement variables. PCA preserves approximately 9 0% of variance while reducing feature complexity, allowing the Random Forest to effectively characterise adoption behaviour. In comparison to conventional classifiers such as Logistic Regression, SVM, and Decision Trees, the suggested model improved performance, attaining 96.7% accuracy, 95.8% precision, 97.1% recall, 96.4% F 1-score, and an AUC-ROC of 0.982. The findings exceed all chosen baselines, demonstrating the system's resilience and reliability. The approach provides behavioural insights essential for policy formulation and strategic engagement by pinpointing the most significant adoption determinants. This research greatly advances the digital banking sector by integrating data science with social impact, providing a clear, high-performing solution to inform financial inclusion policies. It establishes a basis for the development of future real-time and personalised adoption prediction systems utilising advanced AI methodologies. 2025 IEEE.
Source
2025 IEEE International Conference on Emerging Trends in Computing and Communication, ETCOM 2025;
Date
01-01-2025
Publisher
Institute of Electrical and Electronics Engineers Inc.
Subject
Behavioural Prediction; Digital Access; Digital Trust; Dimensionality Reduction; Ensemble Classifiers; Financial Behaviour Analysis; Financial Inclusion; Indian Banking; Machine Learning; Mobile Banking; Payment Bank Adoption; PCA; Principal Components; Random Forest
Coverage
Ramana N.V., Madanapalle Institute of Technology and Science Deemed to be University, Department of Management Studies, Andhra Pradesh, Madanapalle, India; Kumar M., Maharshi Markandeshwar Institute of Management, MMDU, Department of Management, Mullana, India; Vidyashree D.V., Christ University, Department of Commerce, Karnataka, Bengaluru, India; Chand Basha S., St. Ann's College of Engineering and Technology, Department of Business Administration, Andhra Pradesh, Chirala, India; Pandey V., Amity University Patna, Department of Management, Bihar, India; Kaliappan S., Lovely Professional University, Division of Research and Development, Punjab, Phagwara, India
Rights
Restricted Access; Hardcopy may be available in the library
Relation
ISBN: 979-833158508-2;
Format
online
Language
English
Type
Conference paper

Citation

Ramana, Nagella Venkata; Kumar, Mukesh; Vidyashree, D.V.; Chand Basha, S.; Pandey, Vivekanand; Kaliappan, S., “Financial Behaviour Analysis for Payment Bank Adoption Using Random Forest and PCA: An Indian Perspective,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 18, 2026, https://archives.christuniversity.in/items/show/25836.

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