Solving the Credit Impasse: How Big Data and AI are Generating Funding Opportunities for Smallholder Farmers in Africa
Agriculture finance represents an important element of eradicating extreme poverty and boosting shared prosperity. According to the International Fund for Agricultural Development, smallholders manage over 80% of the world’s estimated 500 million small farms and provide over 80% of the food consumed in a significant part of the developing world, making a major contribution to poverty reduction and food security. Approximately 2.5 billion people live directly from agricultural production systems, either as full- or part-time farmers, or as members of farming households that support farming activities. Most smallholder farms are in Asia and sub-Saharan Africa, and in both regions over 80% of farmland is managed by smallholders. Even though these farmers are generally characterized by limited resources—particularly in terms of land—and dependence on household members for farm labor, they represent a critical part of food systems in developing countries.
Why Agricultural Finance is at an Impasse
In light of the size and importance of the smallholder farming sector, the development community has a growing focus on providing these farmers with the funding they need to thrive. The benefits of agricultural finance are boundless, and include boosting employment, improving food security, and growing income for farmers and agricultural small and medium enterprises (SMEs) across the value chain. Increasing access to finance can be especially transformative in Africa, where less than 3% of total bank lending goes to a sector that accounts for about 70% of all employment and over 40% of the GDP. This lack of financing is a major barrier to growth in Africa’s agriculture sector, particularly when it comes to smallholders.
However, addressing this challenge is easier said than done. Farmers and their businesses often lack collateral, and banks struggle to price the risk of loans to smallholders and agricultural SMEs, despite the crucial role they play in rural economies. As a result, interest rates on loans in several countries are extremely high, up to 47% in some cases.
Access to credit is particularly limited for smallholder farmers with no (or thin) credit histories. Financial service providers (FSPs) have traditionally relied on historical financial data such as loan repayments, savings and deposits activity, bill payments, salary slips and other evidence of financial behavior to assess the creditworthiness of a potential borrower. As most farmers lack this data, the situation is at an impasse. Farmers don’t have access to credit, therefore they cannot purchase resources to improve the sustainability or profitability of their operations. FSPs don’t have credit history data, therefore they cannot provide loans without an accurate assessment of the farmers’ financial viability.
Data-Driven Solutions to the Credit Challenge
However, providers may now have the tools to overcome this impasse. Big data and data-driven financial innovations are reshaping the challenge of credit access in new ways. Alternative data – including social media activity, email usage, utility payments history, mobile phone records and psychometric testing – is reducing the need for traditional credit reports. Coupled with advanced analytics tools, this data creates an opportunity to better understand clients who are otherwise excluded from the formal financial sector, and who therefore have to resort to the informal sector. Moreover, data-driven innovation extends well beyond credit analytics into applications that can improve FSPs’ operational performance and management, such as customer segmentation, engagement and insights; client onboarding; product design; fraud prevention; identity verification; and the overall expansion of service offerings to traditionally excluded segments.
Additionally, artificial intelligence (AI) and machine learning are helping deliver better business insights from big data, pulling actionable information from billions of observations of transactions and characteristics, which can be used to help design and tailor financial products and services. Rising mobile-phone penetration and improvements in satellite and other forms of data communication also present another solution to this challenge. These new tools can generate a significant volume of data on smallholder farmers.
A new generation of enterprises is leveraging these innovations for the benefit of farmers. For example, FarmDrive, a Kenyan agritech startup, uses mobile phones, alternative data and machine learning to close the critical data gap that prevents FSPs from lending to creditworthy smallholder farmers. Also in Kenya, Apollo Agriculture leverages advances in machine learning, remote sensing and mobile money to deliver input finance and agronomic advice to smallholders at a lower cost than current solutions. Farmers sign up via mobile phone and Apollo then gathers an array of data about each customer, including satellite data, which they use to build a detailed picture of each farmer’s agricultural and economic life. This includes insights like farm and house sizes, crops planted, and yields produced, which are combined using machine learning to assess credit risk.
Financial Services Adapt to the Digital Age
As financial services increasingly go digital, finding innovative ways to utilize new kinds of data and technological tools is fast becoming a necessity for FSPs. The data explosion, combined with a growing capacity to collect, structure and analyze it continually and in real time, is fundamentally transforming the financial services landscape. In the process, it’s giving rise to new opportunities for banks, fintech’s and others to reach vast new markets, and to deepen financial inclusion.
Moving forward, technology will play an increasing role in modernizing the processes and digital infrastructure of FSPs – but there are some key challenges they’ll have to address to leverage data for financial inclusion. Arguably, the most pressing challenge involves consumer protection and data privacy. Fragmented regulatory requirements – both between different countries, and between local governments within countries – make it difficult to ensure consumer protection. More than 120 data protection laws exist around the world, with varying objectives and enforcement mechanisms. Given different economic and cultural contexts, data regulations can be expected to differ across countries and regions. However, fragmented regulations, particularly for security, impose significant costs, including organizational inefficiency, uncertainty about jurisdictional oversight and uneven participation in global data flows. For that reason, it’s critical to adopt global customer data principles to protect the privacy of consumers and avoid discrimination based on sensitive characteristics. As the financial services sector continues its data and tech-driven outreach to smallholder farmers, consumer protection and data privacy must remain at the forefront, particularly in segments of the population that lack financial and data literacy.
If FSPs want to serve smallholders and remain competitive in traditional markets, they will have to continue to invest in new technology initiatives to understand and predict customer behavior, as well as drive operational changes. Impact investors, donors and development organizations can work together with FSPs and data service providers to play a key role in this process, enabling providers to use big data and AI to expand access to credit for smallholder farmers, particularly women and other underserved groups.
Author’s Note: The views expressed in this article are my own and not that of my agency.
Iftin Fatah is a Senior Investment Officer at the U.S. International Development Finance Corporation.
Photo provided by author. Credit: Feed the Future.