AI, cultural diversity, structures and role of microfinance in Africa

27 May, 2024 - 09:05 0 Views
AI, cultural diversity, structures and role of microfinance in Africa

The Sunday Mail

Nixon Chekenya

AFRICA has a US$300 billion credit gap.

Seven of the top ten fastest growing economies are from Africa.

Africa is the second fastest growing region after Asia.

By 2100, a third of the world’s population will live in Africa.

That means a lot of entrepreneurs are likely to emerge from Africa than in any other continent given the vast number of problems we have in Africa.

These problems can be turned into business opportunities by simply solving the problem and getting a decent return for doing that.

Africa is the future.

Problems like starvation, poverty, cash shortages, health crises, lack of proper and adequate housing and clean energy are human needs that can be turned into business opportunities.

However, financial institutions in Africa currently do not have the technologies and risk models to harness the region’s potential effectively.

Africa is rich in terms of resources and cultural and ethnic diversity characterised by people from different walks of lives and varying lived experiences, skills and abilities.

This should help create robust business and financial ecosystems in the region.

The traditional approaches currently used to evaluate households’ creditworthiness and risk profiles completely ignore the culture realities and social structures of ethnic and religious groups across the continent.

As aptly put by Professor Arthur Mutambara, “the way you service New York is not the same way you should service Soweto or Harare.”

Instead of being ashamed of their history and cultural and ethnic structures, Africans must leverage on their culture and social structures to build robust business and financial ecosystems.

Tribes are a key future of Africa in countries like Cameroon.

Africa has thousands of ethnic groups and more than 2 000 different languages.

This cultural diversity makes for a complex but rich landscape.

If one wants to provide financial products in these markets, they really need a deep understanding of the nuance and history and respect the elegance required to code the financial products. Artificial intelligence and proprietary technologies can help to embed ethnic and cultural data into the risk models and algorithms into the financial products.

For microfinance businesses, a typical client runs an informal business.

Her source of labour supply is family members.

She doesn’t keep a balance sheet and may be lacking a proper business plan.

It’s likely that she is financially illiterate in the sense that she may be able to read and write but is not formally educated in business.

This type of applicant is usually turned down by banks and ends up with microfinance institutions.

Instead of asking the same questions a bank can ask, a microfinance company may ask additional questions like language, dialect, family structure, personal and professional relationships, number of wives, tribe plus some other variables that may be needed to understand African business risk.

Most ethnic groups in Africa, like the Barambu, have a rich agricultural way of life.

They are driven by their social standing and operate in a cash economy with close ties to informal loans and savings groups such as Ijenges.

Traditional banks do not extend credit to these groups because of lack of collateral and risk and lack of information.

If a microfinance company wants to assess the risk of these groups, they may want to evaluate their relationship with council or tribal leaders, not necessarily their banks or balance sheets.

These groups are compelled by their family and tribal obligations and they are very careful about their money or business.

They dare not lose their social standing because it will be equivalent to losing a credit score.

The risk of default on a microfinance loan will be lower in these circumstances despite the lack of formal collateral.

*Nixon Chekenya is a lead research fellow & teaching assistant at the Department of Agricultural & Applied Economics (W. Davis College of Agricultural Sciences & Natural Resources)

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