AI-Driven Financial performance evalution of indian banks using explainable machine learning and hybrid multi-criteria decision-making

binay maji

doi:10.36948/ijfmr.2026.v08i04.85496

AI-Driven Financial performance evalution of indian banks using explainable machine learning and hybrid multi-criteria decision-making

Author(s)	Dr. binay maji
Country	India
Abstract	Abstract The rapid digital transformation of the Indian banking sector has significantly increased the availability of financial and operational data, creating opportunities for artificial intelligence (AI)-based decision-making. Conventional financial performance evaluation methods primarily rely on ratio analysis and statistical techniques, which often fail to capture complex nonlinear relationships among financial indicators and provide limited interpretability for stakeholders. This study proposes an integrated framework combining Explainable Machine Learning (XML) and Hybrid Multi-Criteria Decision-Making (MCDM) techniques to evaluate the financial performance of Indian banks. The proposed framework employs machine learning algorithms to predict overall financial performance while incorporating Explainable AI (XAI) techniques, including SHAP (SHapley Additive exPlanations), to interpret feature importance and improve transparency. Furthermore, the study integrates the Analytical Hierarchy Process (AHP) for determining criteria weights and the Technique for Order Preference by Similarity to Ideal Solution (TOPSIS) for ranking banks according to multiple financial indicators. Financial variables such as Return on Assets (ROA), Return on Equity (ROE), Capital Adequacy Ratio (CAR), Net Interest Margin (NIM), Gross Non-Performing Assets (GNPA), Cost-to-Income Ratio (CIR), Liquidity Ratio, and Credit-Deposit Ratio (CDR) are considered in the proposed evaluation framework. The integrated AI–MCDM approach provides a transparent, data-driven, and explainable decision-support system that enhances managerial decision-making, regulatory monitoring, and investment analysis. The findings are expected to demonstrate that combining explainable machine learning with hybrid MCDM produces more reliable and interpretable financial performance assessments than conventional approaches. The proposed framework contributes to the growing literature on AI-enabled financial analytics and offers practical implications for bank managers, investors, policymakers, and financial regulators in India's evolving digital banking ecosystem. Keywords: Explainable Artificial Intelligence (XAI), Financial Performance Evaluation, Indian Banks, Hybrid MCDM, Machine Learning
Keywords	Explainable Artificial Intelligence (XAI), Financial Performance Evaluation, Indian Banks, Hybrid MCDM, Machine Learning
Field	Mathematics > Statistics
Published In	Volume 8, Issue 4, July-August 2026
Published On	2026-08-14
DOI	https://doi.org/10.36948/ijfmr.2026.v08i04.85496

View / Download PDF File

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

International Journal For Multidisciplinary Research

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

AI-Driven Financial performance evalution of indian banks using explainable machine learning and hybrid multi-criteria decision-making

Share this