Bridging the Agricultural Credit Gap: AI-Based Lending Through Weather and Crop Intelligence in Maharashtra
Mrs.Chhaya Amol Patil
Research Scholar,
Agricultural Development Trusts
Shardabai Pawar Mahila Mahavidyalay,
Shardanagar, Baramati
Mob- 9403161492
Email id – capatil3060@gmail.com
Abstract :
Access to timely and affordable agricultural credit is a cornerstone of rural development, especially in agrarian economies like Maharashtra, India. Despite multiple government initiatives and the proliferation of formal financial institutions, a significant portion of small and marginal farmers remain outside the reach of institutional credit. Conventional credit appraisal mechanisms rely heavily on historical repayment records, formal income proof, land ownership documents, and collateral—all of which many farmers lack. These barriers are particularly problematic in regions with low financial literacy, fragmented landholdings, and high climatic vulnerability, such as the Marathwada and Vidarbha regions.
This research investigates the development and efficacy of an AI-based lending mechanism that incorporates weather forecasts, crop yield predictions, soil health intelligence, and remote sensing data to more accurately assess farmer creditworthiness. Using a dataset of 7,500 farmers across five cropping seasons (2017–2022), this study compares the performance of traditional logistic regression models against advanced machine learning algorithms such as XGBoost and Random Forest. Input features include rainfall deviation, NDVI anomalies (from satellite imagery), soil fertility scores, land size, crop type, irrigation access, and historical loan records.
The statistical results reveal that the AI-enhanced model significantly outperforms traditional scoring systems across all metrics. The AI model achieved an accuracy of 87%, an AUC-ROC of 0.89, and improved the precision and recall of default prediction by over 20% compared to baseline models. More notably, it increased credit inclusion by 18%, indicating that many previously excluded yet viable borrowers could be correctly identified as creditworthy using this approach. Key predictors identified included rainfall deviation, NDVI anomalies (a proxy for crop health), and soil fertility indicators, suggesting that dynamic, real-time agro-climatic data are strong determinants of repayment capability.
By leveraging real-time data and predictive analytics, this approach offers financial institutions a reliable and scalable model for risk assessment in agricultural lending. Beyond improving loan performance, the model also promotes financial inclusion, reduces dependency on informal lenders, and strengthens rural resilience to climate variability.
The findings advocate for the integration of AI-driven tools in the agricultural credit ecosystem through partnerships between banks, meteorological agencies, space research organizations, and agritech startups. With appropriate safeguards, transparency, and farmer outreach, this approach has the potential to transform credit accessibility in Maharashtra and serve as a model for other agrarian states facing similar challenges.
DOI link – https://doi.org/10.69758/GIMRJ/2509I9VXIIIP0023
Download