Customer-centric AI in Banking: Using AIGC to Improve Personalized Services
Bibliographic record
Abstract
Based on the development status of the banking industry in the United States, this paper discusses the application value of artificial intelligence generation content (AIGC) technology in the personalized banking service. The research adopts the methods of literature review and case analysis to analyze the technical characteristics of AIGC and its application potential in the fields of content generation and intelligent interaction, and focuses on the path of AIGC to realize personalized service scenarios such as intelligent customer service, intelligent investment, precision marketing, risk control and compliance. By sorting out the best practices of AIGC application in the US banking industry, the research believes that AIGC is the key grasp and enabling technology of the banking industry to provide personalized services centered on customers. The US banking industry should accelerate the pace of AIGC and business integration, and deepen the service process and digital transformation with AIGC. Grasping the development opportunities of AIGC requires the coordination of technology, talent, process, risk control and other aspects. AIGC has a promising future in the field of personalized banking services.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".