AI-Enabled Intelligent Chatbot Ecosystem for Enhancing Customer Experience in Digital Banking Operations
Bibliographic record
Abstract
The rapid digital transformation of banking services is creating a premise for intelligent, secure and personalized solutions to interact with the customers. In this paper, we propose an AI-enabled chatbot ecosystem to improve the customer's experience in the electronic banking services. Context is integrated into the system with real-time recommendations, rule based natural language processing (NLP) modules, compliant context-based responses, reinforcement learning-based curriculum design and compliance monitoring and fraud detection and prevention. Experimental evaluation shows that our proposed ecosystem endowed with much better performance than existing chatbot models. The developed framework achieved an accuracy of 96.8 %, and a precision of 95.1 %, recall 94.5%, and F1-score of 94.8 % with the reduced-response-time (RTR) of 150 ms. The customer satisfaction index was 92 %, the intent recognition rate 96 % - in other words, it is very reliable and efficient. The results demonstrate the system's capability to provide the indispensable adaptability and security for secure and seamless interaction while reducing the operational costs and enhancing trusted interaction. The proposed ecosystem positions AI-enabled conversational agents as a strategic enabler for future new digital 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".