International Journal For Multidisciplinary Research

E-ISSN: 2582-2160   •   Impact Factor: 9.24

A Widely Indexed Open Access Peer Reviewed Multidisciplinary Bi-monthly Scholarly International Journal

Call for Paper Volume 8, Issue 4 (July-August 2026) Submit your research before last 3 days of August to publish your research paper in the issue of July-August.
Submit Research Paper Join as a Reviewer

Plagiarism is checked by the leading plagiarism checker TurnItIn

Call for Paper

Volume 8 Issue 4
July-August 2026

Submit your research paper

Indexing Partners
Academia Advanced Sciences Index Bielefeld Academic Search Engine CiteSeer DRJI Google Scholar Independent Search Engine & Directory Network ISI (International Scientific Indexing) Issuu Mendeley Research Networks
RefSeek ResearcherId - Thomson Reuters ResearchGate Scirus Scribd Semantic Scholar UTeM - Universiti Teknikal Malaysia Melaka Wiki for Call for Papers WorldCat Zenodo

Revenue Management through Risk-Based Pricing in Digital Banking: A Data-Driven Framework for Sustainable Profitability

Author(s) Dr. EKTA SINGHA ROY
Country India
Abstract The rapid digitalization of banking has fundamentally transformed traditional revenue management strategies. Conventional pricing mechanisms based on fixed interest rates and standardized customer segmentation are increasingly being replaced by dynamic risk-based pricing models supported by artificial intelligence (AI), machine learning (ML), big data analytics, and digital banking platforms. Risk-based pricing enables financial institutions to align loan pricing with individual borrower risk, thereby improving profitability while maintaining competitive lending practices. This study investigates how digital banking technologies facilitate revenue optimization through risk-based pricing mechanisms.
Using a quantitative research design, the study examines the influence of customer credit risk assessment, AI-enabled credit scoring, digital transaction analytics, and dynamic pricing on revenue performance in commercial banks. Primary data collected from banking professionals and digital banking customers are analyzed using Structural Equation Modeling (SEM). The proposed conceptual framework integrates Revenue Management Theory, Information Asymmetry Theory, Dynamic Pricing Theory, and Resource-Based View (RBV).
The findings are expected to demonstrate that digital risk assessment significantly improves pricing accuracy, reduces loan default rates, increases interest income, and enhances overall revenue management efficiency. The research contributes to both academic literature and banking practice by proposing an integrated digital pricing framework for sustainable financial performance.
Keywords Revenue Management, Risk-Based Pricing, Digital Banking, Artificial Intelligence, Credit Scoring, Machine Learning, Dynamic Pricing, Banking Analytics, FinTech, Revenue Optimization
Field Mathematics > Economy / Commerce
Published In Volume 8, Issue 4, July-August 2026
Published On 2026-07-21
DOI https://doi.org/10.36948/ijfmr.2026.v08i04.84187
View / Download PDF File

Share this



E-ISSN 2582-2160
CrossRef

CrossRef DOI prefix of IJFMR is 10.36948/ijfmr

Downloads
Research Paper Format Copyright Permission Form and Undertaking Form

All research papers published on this website are licensed under Creative Commons Attribution-ShareAlike 4.0 International License, and all rights belong to their respective authors/researchers.

CC-BY-SA Open Access

About IJFMR
Fees & Payment
Current Issue
Publication Archive 		Submit Research Paper
Track Submission Status
Publication Guidelines
Publication Ethics
Peer Review & Plagiarism 		Join as a Reviewer
Editors & Reviewers
Reviewer Referral Program
Get Reviewer Membership Certi. 		Website/Journal Policies
Usage Policy
Content Policies
Privacy Policy
Contact Us +91-9687-828-838 editor@ijfmr.com
Powered by Sky Research Publication and Journals