Predicting Financial Solvency of Commercial Borrowers: The Case of Non-Banking Financial Companies
Bibliographic record
Abstract
Credit risk can be effectively managed by evaluating and predicting the credit worthiness of a customer or a corporate. Credit scores are calculated to assess the credit worthiness. It helps the financial institutes to know the amount and dimensions of risk involved in different credit transactions. Credit scoring helps the financial institutes to decide whether or not to lend. It also helps in deciding the price of a particular exposure, the appropriate credit facility and different risk tools. This research paper focuses on identifying the triggers of credit default. It also focuses on checking and predicting the financial solvency of the borrowers of non-banking financial companies and assigning the credit worthiness to these companies. The data is collected from a Mumbai based NBFC. The data for the study are extracted from balance sheet and profit &loss statement of these companies. The data includes the financial ratio variables for forty companies. Altman's Z-score is used to find credit worthiness and DuPont technique is used to find the main causes of financial distress. The results of this research highlights that the borrowing companies having a lower return on equity (ROE) are prone to be in distress zone. This research would help the financial institutions to identify the most likely defaulter companies and to segment the clients/companies in safe, grey and distressed zones. The results are robust to sub-samples.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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 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".