Design of Cloud Collective Financial Robot Based on Artificial Intelligence Technology
Bibliographic record
Abstract
In this paper, we design a cloud-integrated financial robot based on artificial intelligence technology to provide advanced financial analysis and decision support for financial institutions.The robot platform is embedded with a large amount of financial domain knowledge and data, which can provide uninterrupted financial services to customers using a dialog engine.At the same time, it is equipped with the attention mechanism -long and short-term memory neural network model, in investment transactions and credit risk prediction, which can bring a new digital intelligence experience for financial institutions.The standard and similar sentence recognition accuracy of the article robot dialog engine can be stabilized at more than 90%, and the average access time of the user's access request is about 0.15s.The importance distribution of financial credit risk indicator features is 5~24, and when the number of features takes the value of 10, the risk prediction accuracy of the robot in this article is the highest, 97.98%.When the prediction model is trained to 50~70 epochs, the Loss value of the financial robot converges to 0.15~0.17.The accuracy of the model chosen in this paper for risk prediction as well as stock prediction is 95.35 and 96.2% respectively.And the absolute difference between the predicted stock price and the true value of the model in this paper is 0~0.28 yuan.Combined with DMI strategy for stock trading, the return is 30.7%.The financial robot improves the user experience and increases the value of risk control as well as stock prediction for financial institutions.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| 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".