AI-Driven Credit Scoring Model in Smarter Lending Decisions for Farmers
Bibliographic record
Abstract
Millions of small and marginal farmers in India encounter obstacles such as limited access to markets, inconsistent income, inadequate collateral, and restricted financing alternatives, which are further aggravated by fragmented supply chains and the lack of a cohesive digital infrastructure. This research introduces an AI-driven credit scoring system that utilizes nontraditional data sources, including soil health, crop history, and weather patterns, to assess customized credit scores, allowing farmers to obtain loans even in the absence of formal credit histories. Two neural network models were created and evaluated for loan approval prediction: a Feedforward Neural Network (FNN) and a Deep Neural Network (DNN), using data on employment, credit history, and demographics. Essential preprocessing techniques, such as feature scaling, categorical encoding, and addressing missing values, were applied to prepare the dataset. Both models employed the Adam optimizer and categorical cross-entropy loss, with early stopping implemented to mitigate overfitting. The DNN exhibited superior performance, achieving a test accuracy of 86.18% and a loss of 0.4610 f1-score of 0.84, in contrast to the FNN, which recorded an accuracy of 83% and a loss of 0.4989 and f1-score of 0.80. These results highlight the potential of AI-based systems to transform agricultural financing by enhancing loan accessibility and efficiency for farmers.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| 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".