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TRUVACE RECORD VERSION
record: TRV-2026-2070
version: 1
kind: certified
reason: Certified into the record
timestamp: 2026-10-21T01:10:51.574467Z
status: published
lens: g_space
sector: business
headline: ARTIFICIAL INTELLIGENCE IN BANKING: AN EMPIRICAL STUDY ON AI-POWERED DECISION-MAKING AND STRATEGIC MANAGEMENT EFFICIENCY IN INDIAN COMMERCIAL BANKS
dek: Artificial Intelligence (AI) has emerged as a strategic technology transforming the structure, processes, and decision-making capabilities of the banking sector. Indian commercial banks are increasingly integrating machine learning, predictive analytics, natural language processing, robotic process automation, and intelligent decision-support systems into credit assessment, fraud detection, customer relationship management, risk management, and strategic planning. This empirical study examines the influence of A…
gain_title: AI-powered decision-making, especially decision accuracy and predictive capability, was positively and significantly associated with strategic management efficiency among Indian commercial bank professionals.
problem_title: (none)
trace_subject: (none)
gain_reading: AI-powered decision-making, especially decision accuracy and predictive capability, was positively and significantly associated with strategic management efficiency among Indian commercial bank professionals.
gain_evidence: AI-powered decision-making has a positive and significant relationship with strategic management efficiency | Decision accuracy and predictive capability emerge as particularly influential dimensions
problem_reading: (none)
problem_evidence: (none)
quick_read: An empirical study of Indian public and private sector commercial banks examined how AI-enabled decision-making relates to strategic management efficiency. It looked at four dimensions: decision accuracy, decision speed, predictive capability, and risk-management support.

The result suggests banks may gain most when AI is treated as a strategic decision-support capability rather than only as automation. Uncertainty remains because the text describes the data collection and sample as proposed or considered, and the findings are correlational and perception-based.
limitation: The text says primary data were proposed to be collected and a sample of 400 was considered, so it is unclear whether the reported findings rest on completed measured data; results also rely on professionals' questionnaire responses.
tag: Evidence-backed gain
key_points: The study covers AI uses in credit assessment, fraud detection, customer relationship management, risk management, and strategic planning. | Decision accuracy and predictive capability were the most influential dimensions of AI-enabled decision-making. | AI-enabled risk assessment was reported to strengthen managerial responsiveness and strategic planning. | The authors name AI governance, reskilling, responsible data management, and human oversight as essential.
rundown: The study frames AI as a strategic technology reshaping the structure, processes, and decision-making of banking. Indian commercial banks are described as integrating machine learning, predictive analytics, natural language processing, robotic process automation, and decision-support systems.

The methodology relied on a structured questionnaire for banking professionals, with a sample of 400 respondents and proportionate stratified sampling. Analyses included descriptive statistics, Cronbach's alpha, correlation, exploratory factor analysis, and multiple regression.

The authors conclude that sustainable efficiency and competitive advantage depend on governance, employee reskilling, responsible data management, and human oversight.
sources:
- peer_reviewed | Acta Innovations | https://doi.org/10.62441/actainnovations.vi.1983 | 2026-10-18
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