Alternative Data Can Unlock Agricultural Digital Financial Service: GSMA

In its mobile for development section, GSMA, an association of mobile network operators, published an article that describes the use of alternative data in providing agricultural digital financial services (agri-DFS) and how digital procurement/supply chain platforms can be valuable data sources.

In its mobile for development section, GSMA, an association of mobile network operators, published an article that describes the use of alternative data in providing agricultural digital financial services (agri-DFS) and how digital procurement/supply chain platforms can be valuable data sources.

The article highlights growing digital procurement solutions and their tremendous potential in unlocking data that offer opportunities for capturing digital footprints and economic data that can unlock agri-DFS.

In many countries, financial service providers, including microfinance institutions (MFIs), and savings and credit cooperative organizations are more inclined to serve rural farmers than commercial banks. However, a lack of data on farmers’ activities, income and credit histories limits their ability to predict cash flows and extend financial products such as loans and insurance. The unavailability of data is also a reason why conventional banks do not have products tailored for farmers.

Were such data accessible, it would make borrowing cheaper and help speed up loan processing. 

Still, single digital procurement platforms have yet to gain mainstream acceptance in Ethiopia. A feasible alternative could be data gleaned from digital extension, or even regular extension, services. A GSMA report published in 2020 counted 11 digital agriculture advisory services in the country. 

To test the practicality of using alternative data, GSMA supported a pilot program in Papua New Guinea. Dubbed “rural loan”, the program looked to avail credit to vanilla farmers. It made use of data from a digital procurement platform to forge economic identities for the smallholder farmers and generate credit scorecards to be used as a risk-assessment tool by MFIs. 

The scorecards provided a reliable basis for financial institutions to provide uncollateralized loans. Each scorecard was developed based on three criteria.

-Farmer’s financial history, such as savings and income.

-Demographics – gender, age, marital status, and farm location – were also taken into account.

-Data linked to vanilla production, such as quality scores and order history, comprised the final piece. 

Financial institutions would decide whether to reject a loan application, recommend it for manual review, or approve it based on the scorecard. 

A report published to showcase the key learnings gleaned from the pilot program states credit must be issued to a wide spectrum of farmers, including those that are likely to default, to generate scorecards that reliably predict loan outcomes. The report noted that it was crucial to make more targeted efforts to reach underserved communities such as women, which made up just 4% of the total credit recipients. The report suggests waiving registration fees for women as a solution, as well as better aligning loan terms with the growing season to garner more satisfactory results. 

As with conventional data, considerations need to be made with the use of alternative data. Implementers should ensure data quality at the generation stage for efficiency. Privacy, too, is an important aspect. Data ownership in digitized agriculture value chains can be ambiguous and requires stakeholder clarification before generation, storage, and sharing. Data legislation is key to protecting farmers’ privacy. 

Read GSMA’s full article here.

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