Banking Order Classification and Information Extraction
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
This study presents a system to classify banking orders from customers and to determine the transaction parameters of these orders using machine learning techniques. The presented system uses optical character recognition and shape detection technologies to extract texts and tables from images i.e., scanned email attachments and fax images. Then, in the classification phase, texts are vectorized with the TF-IDF approach after preprocessing and are classified using support vector machines. The orders classified as money transfer are sent to the information extraction module and the parameters of the transaction (sender information, recipient information, amount and description) are determined using named entity recognition methods. Finally, this information is sent directly to an operator’s screen for her to check and confirm the parameters and execute the money transfer operation. This system is implemented in a medium-large scale bank in Turkey. This system, which yields high classification and information extraction performance, is expected to save a significant amount of workload for the bank, speed up the order execution process and increase customer satisfaction. The system is currently deployed and being validated online.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.003 | 0.000 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 it