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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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 teacher head, 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".