Data driven solutions: bridging the financial gap for underserved communities

By Dmitry Borodin, Head of Decision Analytics at Creditinfo

Despite a global push to increase the reach of financial services, certain segments of society remain underserved when it comes to accessing finance. According to Creditinfo data, young individuals and women are the most disadvantaged, with women making up only 27% of contract borrowers in West Africa and only 0.4% out of all 20 to 24 year olds receiving formal loans of over $200 in Kenya in 2023.

With limited or no access to certain financial products and loans, these segments of society often struggle to rent, buy a property, or raise capital to finance a business. It’s crucial, therefore, for financial institutions, regulators and local governments to work together to address the issues preventing certain groups of society from accessing finance and narrow the lending gap. 

In addition to collaboration between these different bodies, data-driven solutions also play a key role in unlocking financial inclusion and bridging this gap. As alternative forms of lending, through micro and mobile loans, and mobile transactions have started to dominate emerging economies, a huge amount of alternative data is now available on underserved groups that can be used to assess eligibility for formal loans. By ethically acquiring and utilising this alternative data, unbiased predictive models can be developed to examine an individual’s creditworthiness.

The underbanked and the obstacles they face

Women and young people are consistently underbanked across multiple geographies. Systemic issues and social complexities, such as a lack of formal credit data, pre-existing bias in financial institutions’ assessment processes, and broader societal prejudices and discrimination, have exacerbated this phenomenon. Traditionally, credit scores are derived from data obtained from regular consumer bank transactions or prior payment records, so the underserved will constantly come up against the same issues while barriers like this remain in place.

This is especially the case in Africa, where 57% of citizens struggle to obtain the data required for a conventional credit score. When faced with social barriers stemming from prejudice and discrimination, women are up against more obstacles than their male counterparts in accessing finance, especially without the right credit data to hand. Similarly, for younger generations, who do not have sufficient bank records, it’s much more difficult to apply for formal loans.

Score-driven solutions and fairness

This is where alternative data comes in. More and more data on unbanked consumers is now available due to a large increase in alternative types of lending. Micro-lending, for example, offers small loans to those without access to traditional financial institutions, and they now represent a very high proportion of lending data in countries such as Kenya, where approximately 25% of 20 to 24 year olds have recently received loans of less than $200. Similarly, we’ve also seen an increase in the prevalence of mobile loans across emerging markets. Both methods offer a wide range of data that could be used to ‘graduate’ underserved communities to access formal credit, providing banks with a reliable lending history. 

Today, almost everyone has a mobile phone which means it’s easier for people to bank online or use mobile wallets. Data from these transactions, when processed in the right way, can inform banks and lenders on customer cash flow and income, acting as another alternative risk indicator for formal loan applications.

With a wide range of data available, there’s a huge opportunity for fintechs and financial institutions to create solutions that benefit underserved customers by combining alternative data sources with predictive models that can assess an individual’s suitability for loans. AI and machine learning models have made great strides in recent years and can be used to analyse and extract actionable insights from a wide range of unstructured data points drawn from these new data sources. 

Bridging the gap: a win-win situation

It’s crucial for lenders to tap into this widening data pool to provide access to credit for underserved demographics. A strong credit score is vital for many reasons, from purchasing or renting a property to signing up for television or mobile contracts. Removing barriers to finance especially for women, will help to close the gender lending gap. If more women have access to business loans, they will have capital to recruit, expand their service or product offering, and generate new revenue streams.

Ultimately, financial inclusion is essential for the overall health of the economy. With rising inflation and the global economic challenges of recent years, countries should be taking the necessary measures to protect their economies. By increasing access to finance for underserved segments of society, governments and banks have the opportunity to increase participation in the local economy, by supporting individuals in setting-up or growing their small businesses. This local participation, in turn, encourages wider and more sustainable economic growth at a national and regional scale.


Explore more