Picture: ISTOCK
Picture: ISTOCK

Many South African businesses situated outside of the urban periphery rely on a cash-based system to remain operational. This is due to many factors, but one of the most prevalent aspects is a lack of exposure to banking channels.

Communities in remote areas do not have regular access to conventional banking platforms such as automatic teller machines or physical bank branches to access salaries. These communities also do not use virtual purchases and payments.

Historically, banks have tried to bridge this gap by offering "cash back", a form of withdrawing money from bank accounts at retail tills, and more recently by offering online and cellphone banking.

One of the influences obstructing the usage of online banking in rural areas is erratic data coverage. Not only is stable coverage unlikely where it is most needed, but it is sporadic due to a lack of robust infrastructure.

Due to the security precautions in place over online banking, the time spent waiting for signal to reconnect to a user’s cellphone could lead to the user being automatically signed out of their banking account, further frustrating their online banking experience.

This lack of virtual banking exposure has contributed to the absence of financial transaction history for millions of South Africans.

The knock-on effect is the increasing gap in the number of emerging entrepreneurs who require access to credit and the number of entrepreneurs whose credit applications are approved.

It seems this problem is not being recognised by financial institutions.

A crucial input to starting an enterprise is the startup capital that facilitates the development of a business idea. In the absence of savings, entrepreneurs rely on loans to generate startup capital. However, entrepreneurs making profits from nonconventional means cannot fit into the traditional credit mould in the credit application process.

Traditional banks use credit bureaus that issue credit scores on individuals to determine whether an applicant is credit-worthy. In emerging economies, the data used in these scores is often unavailable, incomplete or inaccurate. Although bureaus use different metrics to determine an individual’s credit score, which can result in conflicting ratings, the standard measures of credit are based on access to collateral and how applicants have paid off credit in the past. In lieu of this, banks will generally accept three months’ bank statements as proof of an entrepreneur’s ability to generate cash.

Additional hindrance is caused by the fact that the credit scores issued by bureaus, although specific to an individual, are not tailored to the individual. Metrics are included that may be entirely unrelated to the economic sector that an entrepreneur operates in, and thus are counterintuitive to a fair scoring process. This problem is widespread throughout financial institutions that use standardised measures of credit ratings.

In the agricultural sector, this is further exacerbated. Farmers rely heavily on environmental conditions to turn a profit. An outbreak of a lethal or costly disease in a herd, or a yearlong drought as recently experienced in South Africa is detrimental and hard to predict and protect against.

Agriculture makes up a significant portion of the African economy, and yet it is a difficult sector to stimulate due to investments being exceptionally risky, albeit exceptionally necessary.

Credit is further stifled in the agronomic environment due to the seasonality of returns. Farmers struggle to demonstrate a steady stream of income to match their steady drain of expenditure.

Agricultural lenders list a lack of visibility of finances as a burden to being able to approve credit. This is a significant factor in the emerging farming sector, largely driven by the "grey money" that makes up most of the rural economy.

An alternative solution for agriculturalists is to join a cooperative that assists them in accessing credit. However, a necessity for membership to such an organisation is owning the land that the farmer manages. In many African countries, the women of the household operate the farming enterprises. In Kenya, about 80% of the farms are women-run.

Nevertheless, in many cases, land is owned by the men of the family due to archaic inheritance culture that has changed marginally in the last century. Without ownership of their land, women in rural areas are unable to expand their operations through cooperatives and must rely on formalised credit.

The government, and more specifically the Land Bank, endeavour to combat this problem domestically by boosting agricultural reform through land reclaims and farm grants for suitable candidates. Although this is a step in the right direction, emerging farmers are given little financial assistance once the title deed of the land has been transferred. With no further credit to buy seed, equipment, livestock or feed, small-scale farmers find it challenging to stay afloat.

Fortunately, a few green shoots have begun to emerge. In 2014, Rita Kimani and Peris Bosire founded FarmDrive in Kenya. Their seemingly simple business model to create alternative credit scores for farmers is built on a much more complex algorithm. Their team uses data from across the globe to compute their scores.

FarmDrive assists farmers in tracking their farming activities such as income, expenditure and yields via SMS (a more reliable platform than online transmissions) creating a real-time history of their enterprise.

Kimani and Bosire have also incorporated individually unique aspects that would affect the repayment of a loan such as agronomic, environmental and satellite statistics. The code incorporates factors such as the rainfall and fertility of the farm’s location to determine the ability of the farmer to pay back a loan. These inputs assume that favourable weather and soil conditions increase yield, and correspondingly, the likelihood of farmers being able to meet loan installments.

Other data-driven inputs such as the farmer’s social-media interaction and literacy (based on their SMS interaction), also enable FarmDrive to better understand the skills level of the applicant. Theoretically, the more educated the farmer, the less risky making a loan to such a farmer will be.

FarmDrive then passes on the relevant credit score and any changes to their aligned financial institutions who assist the farmers in obtaining a suitable credit solution.

FarmDrive depends on its partnered banks accepting its credit scores as reliable. Consequently, applicants must rely on the few banks who have thus far partnered with FarmDrive to provide credit.

After the recent credit ratings downgrade in South Africa, many are turning to the government and established institutions to provide a resolution to the lack of economic growth in the country.

In a primary sector such as agriculture, growth achieved is theoretically compounded along the value chain. As produce and livestock numbers increase, companies that operate in logistics, animal feed, auction environments, meat processing and retailers should experience similar growth.

Considering this, and that the agricultural lending environment in Kenya is largely comparable to that of South Africa, there is no reason to believe South African agricultural banks would not accept an adaptation of FarmDrive’s credit scores once their algorithm is wholly explicated.

This would provide meaningful assistance, if not a comprehensive solution, to the lack of emerging agricultural funding in South Africa and stimulate sustainable economic growth.

There is the potential for FarmDrive’s progressive credit scoring to be tailored to other sectors that cannot comply with traditional credit parameters.

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