Agri-fintech: Transforming agriculture finance through digital lending, crop insurance, and farmer adoption

Introduction

Agriculture is an important part of the rural economy, and farmers require financial resources at different stages of the agricultural production cycle. Expenditure is incurred on seeds, fertilizers, labour, irrigation, machinery, harvesting, storage and marketing. Along with these expenses, farmers face several risks, including unpredictable weather, crop losses, pest and disease outbreaks and fluctuations in market prices. Agricultural finance, therefore, is not limited to providing funds. It also involves making financial services affordable, dependable, accessible and available when farmers need them. Traditionally, agricultural financial services have been provided by commercial banks, cooperative institutions, Regional Rural Banks, insurance companies and NBFCs. These services have often depended on documentation, physical verification and conventional credit-assessment procedures. Such processes can take considerable time and may be particularly difficult for farmers living in rural areas. The increasing availability of smartphones, mobile internet and digital platforms has encouraged the development of Agri-Fintech as a way of improving agricultural financial services. Its applications include digital lending, crop-insurance services, electronic customer verification, automated credit assessment and data-based financial support.

In agriculture, this transition creates opportunities to make financial services more accessible to farmers who may otherwise face geographical, procedural or administrative difficulties. Digital systems can potentially reduce the need for repeated physical visits and make certain financial processes faster and more convenient. These developments have contributed to the growing role of technology in agricultural finance.

Background and Research Gap

Agricultural finance has an important practical requirement: financial services need to reach farmers at the right time and through procedures that are simple, dependable and easy to access. Delays in receiving credit or completing insurancerelated processes can create difficulties for farmers because agricultural activities are closely linked to crop seasons and production schedules. The present study therefore considers the issue from two perspectives. At the institutional level, it examines whether digitalization is associated with measurable changes in the efficiency of agricultural financial operations. At the farmer level, it examines whether these developments are reflected in awareness and actual use of digital financial services. Existing literature has examined various aspects of financial inclusion, FinTech, digital banking, financial literacy and rural financial services. Earlier studies have also generally approached digital financial inclusion and FinTech from a broader perspective. Fewer studies have directly compared operational measures before and after the adoption of digital systems in agricultural finance. The present study attempts to address this gap by combining institutional and farmerlevel evidence from Raigad district of Maharashtra. This approach provides a broader understanding of Agri-Fintech by examining both changes in institutional operations and the extent to which farmers are aware of and actually use digital financial services.

Need and Scope of the Study

The need for this study is closely related to the timesensitive nature of agricultural finance. Farmers require financial resources at specific stages of crop production, and delays in obtaining credit may affect the purchase of inputs and the timely completion of farm operations. Crop insurance also involves time-bound activities because enrollment, documentation and verification generally need to be completed within specified periods.The study focuses on both farmer-level and institutional aspects of agricultural finance in Raigad district of Maharashtra. The farmer component covers 59 smartphone-using farmers from different villages. It examines their awareness of digital financial services, their actual use of these services and the barriers that may prevent adoption. The institutional component includes 30 respondents from banking and Agri-Fintech institutions. It examines the availability of digital financial services and assesses changes in selected operational indicators before and after digital adoption. These indicators include farmer-verification efficiency, crop-insurance penetration and different aspects of agricultural lending efficiency. By examining both groups, the study provides a combined view of the development of Agri-Fintech. It considers not only whether digital systems can improve institutional processes but also whether farmers are sufficiently aware of and able to use these services.

Objectives of the Study 1. To assess the impact of FinTech innovations on crop insurance penetration and verification efficiency. 2. To measure the reduction in loan processing times and costs using digital lending models in agriculture. 3. To assess awareness among farmers regarding Agri-Fintech services and platforms. 4. To identify key barriers to Agri-Fintech adoption.

Hypotheses

Null hypothesis: FinTech innovations have no significant impact on crop insurance penetration and farmer verification efficiency.  Alternative hypothesis: FinTech innovations have a significant positive impact on crop insurance penetration and farmer verification efficiency.  Null hypothesis: There is no significant reduction in loan processing time and processing cost after adoption of digital lending models in agriculture.  Alternative hypothesis: There is a significant reduction in loan processing time and processing cost after adoption of digital lending models in agriculture.

Review of

Research Gap Emerging from the Literature

The existing literature provides considerable support for the role of FinTech in improving financial inclusion, but several areas remain less explored in an agricultural context. First, there is limited agricultural-specific evidence directly connecting digital financial systems with crop-insurance penetration and the speed of farmer verification. Second, relatively little direct before-and-after evidence is available on changes in agricultural loan-processing efficiency and processing costs following digital adoption. A further issue concerns the difference between awareness and actual use

Research Methodology Research Design

The study employed exploratory and descriptive research designs. The exploratory approach was used for the farmer survey because awareness and adoption are influenced by perceptions, familiarity with technology and personal experience. The descriptive approach was used for the institutional survey because it collected structured information on service availability, processing timelines and administrative costs.

Area of Study

The study was conducted in Raigad district of Maharashtra.

The district provides a relevant setting for examining agricultural finance because its agricultural economy is influenced by coastal conditions and dependence on the monsoon. Crops such as paddy and horticultural crops are important in the area. These characteristics make timely access to credit and crop insurance particularly relevant to farming households.

Sampling Design

The first respondent group consisted of 59 smartphoneusing farmers selected from different villages of Raigad district. The sample included respondents with differences in age, educational background and landholding size. The second group consisted of 30 respondents from banking and Agri-Fintech institutions, including branch managers, loanevaluation officers, agricultural field supervisors and technical staff involved in agricultural lending and cropinsurance-related activities.

Sources and Collection of Data

Primary data were collected during 2026 using structured questionnaires and direct interaction with respondents. Separate questionnaires were prepared for farmers and institutional respondents. Google Forms were used where respondents were geographically dispersed. The farmer questionnaire covered demographic characteristics, smartphone use, awareness of FinTech and digital crop insurance, knowledge of digital agricultural loans, actual use of digital services and barriers to adoption. A five-point Likert scale was used for adoption-barrier statements. The institutional questionnaire collected information about organization type and the availability of digital cropinsurance enrollment, digital customer verification, agricultural loans and digital lending systems. It also included before-and-after measures relating to operational performance. Secondary information was obtained from government reports, academic articles, journals, books, official institutional sources and other relevant literature.

Variables and Analytical Tools

Categorical variables were used for demographic characteristics and service-awareness analysis. Ordinal responses were used to measure adoption barriers, while paired before-and-after observations were used to examine changes in institutional efficiency. The main analytical methods were percentage analysis, Likert-scale analysis and the Wilcoxon Signed-Rank Test.

Percentage Analysis

Percentage = (Number of respondents in a category / Total number of respondents) × 100

Likert-Scale Analysis

A five-point Likert scale was used to assess barriers affecting Agri-Fintech adoption. Responses were coded as Strongly Disagree (1), Disagree (2), Neutral (3), Agree (4) and Strongly Agree (5). The weighted mean was calculated as: Likert Mean Score = Σ(fi × Si) / N, where fi is the frequency of responses and Si is the corresponding score.

Wilcoxon Signed-Rank Test

The Wilcoxon Signed-Rank Test was applied to paired institutional observations. The same 30 institutional observations provided information for both before and after periods, making a paired non-parametric test suitable. For each pair, the difference was calculated as Di = Afteri -Beforei A p-value below 0.05 was treated as evidence of a statistically significant difference.

Results

Socio-Demographic Profile of Farmers

The farmer survey included 59 respondents. The gender distribution comprised 22 males (37 percent), 13 females (22 percent) and 24 respondents who selected "Prefer not to say" (41 percent). Because this was the largest category, the figures were used primarily to describe sample composition rather than to draw conclusions about gender-based differences. The respondents represented all specified age groups. Farmers aged 46-55 years formed the largest group at 29 percent. Overall, 60 percent of respondents were aged 45 years or below.

Landholding Profile

Landholding size varied across the sample. Farmers with holdings of 4-10 hectares represented the largest group at 29 percent, followed by those with 2-4 hectares at 25 percent. Holdings below 1 hectare accounted for 17 percent, while 15 percent had 1-2 hectares and 14 percent had more than 10 hectares. This variation provides context for differences in agricultural financial requirements.

Services Provided by Financial Institutions

The institutional survey included respondents from different types of organizations involved in agricultural finance.

Cooperative banks accounted for 33 percent of the institutional sample, followed by commercial banks at 23 percent. NBFCs represented 17 percent, Regional Rural Banks 13 percent, and Agri-Fintech and insurance companies 7 percent each. Agricultural loans were the most widely available service, reported by 90 percent of institutions. Mobile-based cropinsurance enrollment was available in 83 percent, digital crop-insurance platforms in 70 percent, and digital customer verification and digital agricultural lending systems in 60 percent each. Digital transformation was not uniform; some processes remained hybrid or manual.

Impact of FinTech on Crop Insurance Penetration and Verification Efficiency

Paired observations were collected from 30 institutional respondents to compare reported conditions before and after digital adoption. The Wilcoxon Signed-Rank Test was used to determine whether the observed changes were statistically significant. The mean verification-time rating declined from 3.00 before digital adoption to 2.00 after adoption. The Wilcoxon result was Z = -4.524 with p < 0.001, indicating a statistically significant change at the 5 percent level. The direction of change indicated improved verification efficiency following digital adoption. The mean percentage of insured farmers increased from 44.50 percent before digital adoption to 61.47 percent after adoption, an increase of 16.97 percentage points. The Wilcoxon test produced Z = -4.786 and p < 0.001, indicating a statistically significant difference. The result showed a positive association between the reported use of digital insurance mechanisms and cropinsurance penetration among the institutions covered. These findings suggest that digital enrollment and verification mechanisms may help institutions process insurance-related activities more efficiently while potentially reaching a larger proportion of farmers.

Impact of Digital Lending Models on Loan Processing Efficiency

Six operational indicators were examined using responses from 30 institutional respondents. Mean efficiency scores before and after implementation of digital lending workflows were compared using the Wilcoxon Signed-Rank Test.

Discussion

Farmer Awareness of Agri-Fintech Services

The farmer-level results showed that respondents were more familiar with particular digital financial services than with the technical term "FinTech." Only 39 percent were familiar with the term FinTech. Awareness was higher when questions focused on specific functions: 75 percent knew that digital channels could provide faster credit and 61 percent were aware of digital payments. Practical knowledge was stronger. Seventy-six percent knew how to purchase crop insurance digitally, and 69 percent knew how to apply for an agricultural loan through a mobile or online application. This difference indicates that farmers may understand the purpose of a service and know how to access it without identifying it as FinTech. Only 19 percent of farmers had used a digital platform to purchase or manage crop insurance. Digital agricultural loans and digital payments for the stated farming activities were each used by 24 percent. Thus, more than three-fourths of the smartphone-using farmers were not regularly using these digital financial services. The difference between awareness and actual adoption was a central finding.

Interpretation of the Awareness-Adoption Gap

The findings point to three connected stages in the digitalfinance process: availability of the service, awareness and practical knowledge, and actual adoption. The institutional survey showed relatively high availability for several digital services. The farmer survey also showed substantial awareness of specific digital functions. Yet actual use remained considerably lower. Several factors may contribute to this gap, including security concerns, service charges, connectivity problems, limited confidence in using applications and continued preference for receiving assistance during financial transactions. The results indicate that creating digital platforms alone does not ensure regular farmer adoption.

Key Barriers to Agri-Fintech Adoption

Limitations and Future Research

The study was conducted in one district and used a relatively small sample of 59 farmers and 30 institutional respondents. The farmer component was limited to smartphone-using respondents, so the findings should not be generalized to all farmers. The institutional analysis used reported efficiency ratings rather than transaction-level administrative data. Future research could use larger multidistrict samples and transaction-level records to estimate actual changes in processing time and cost more precisely.

Acknowledgement

AcknowledgmentsThe authors acknowledge the respondents who participated in the farmer and institutional surveys and the individuals who assisted with data collection and field interaction. Institutional support received during the study is also gratefully acknowledged.

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