EMPOWERING BANKING THROUGH AI: ANALYZING CUSTOMER PERSPECTIVES AND ADOPTION DRIVERS IN INDIA
Bibliographic record
Abstract
Background: The future of an economy is largely dependent on the functioning of its banking sector. Artificial Intelligence (AI) has turned up as a dynamic force exerting a significant influence over the banking industry functions. This research looks at the role of AI in banking from a customer perspective. Methods: Primary sources of data collection were used and data was gathered from 250 respondents using a well-structured questionnaire. Analysis was conducted deploying various statistical techniques like descriptive statistics and inferential statistics including correlation analysis, regression analysis and ANOVA. The study proposes a framework to measure the intention to adopt AI in Indian banking system and the factors influencing the adoption decisions. Results: The findings show that intention to adopt AI in banking is influenced by awareness, attitude towards AI, subjective norms, perceived risk, perceived usefulness, and knowledge of AI technology. It yields a positive perception and intention towards AI adoption with perceived usefulness and perceived risk having strong correlation with adoption intention. Recommendations are given to accelerate AI in banking by investing in AI integration, customer trust building and tailored marketing strategies. Concludingly, embracing AI can drive banking innovation, deliver better services, build deeper customer relationships and be competitive in the market.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".