How data driven solutions are helping to bolster financial inclusion for underserved communities 

How data driven solutions are helping to bolster financial inclusion for underserved communities 

In this article by Dmitry Borodin, Head of Decision Analytics at Creditinfo, we dive into how financial inclusion can enhance lives of underserved communities through alternative data solutions. 

Dmitry Borodin, Head of Decision Analytics at Creditinfo

Although there has been a push to widen access to financial services globally, there remain underserved communities who struggle to access loans. Women and young individuals under the age of 25 are some of the most impacted with only 0.4% out of all 20 to 24 year olds receiving formal loans of over US$200 in Kenya in 2023 and women making up only 27% of borrowers in West Africa, according to Creditinfo data. 

This limited access to financial products and loans can have a significant impact on an individual’s lives. They may find themselves unable to raise capital to finance a small business or without sufficient financial records to rent or buy a property, exacerbating existing societal inequalities that these segments of society already face.  

The onus lies with financial institutions, governments, credit bureaus and regulators to collaborate and address these issues to narrow the lending gap. Collaboration can also be supported by using data-driven solutions, which are beginning to play a key role in facilitating access to finance for underserved groups. As emerging economies continue to utilise mobile banking, and alternative forms of lending such as micro and mobile loans, more and more forms of alternative data will be released which can be used to assess formal loan eligibility through the use of unbiased predictive models. 

What are the barriers to financial inclusion? 

For younger people and women, systematic issues are often faced when trying to access formal loans including pre-existing bias in financial institutions’ assessment processes and a lack of formal credit data to support loan requests. Combined with broader societal prejudices and discrimination, this phenomenon is exacerbated. While credit scores continue to be established from data derived from regular consumer bank transactions or prior payment records, the underserved will continue to face the same barriers when it comes to accessing formal loans.  

We see this phenomenon occurring more in emerging economies. In Africa, for example, 57% of citizens have difficulty obtaining the data that will form a conventional credit score. Women are also more likely to face social barriers which originate from pre-existing prejudices. So, without the right data to hand, women face an additional layer of obstacles than their male counterparts.  

Harnessing alternative data 

With a large increase in the use of alternative lending channels in recent years, a wealth of data is being unlocked for unbanked consumers. In countries such as Kenya, for example, mobile lending represents a very high proportion of lending data, with approximately 25% of 20 to 24 year olds recently receiving loans of less than US$200. Mobile loans are similarly popular in other markets and are beginning to incorporate data from millions of unbanked individuals into financial systems. 

Such forms of lending unlock a wide range of data on consumers demonstrating their eligibility for formal loans. For example, Creditinfo data shows that new banking customers are twice less likely to default when they have good history of mobile loans. This alternative data can then be used by financial institutions to ‘graduate’ underserved communities to formal credit, narrowing the financial gap. 

Mobile phone transactions can also be an important indicator in estimating credit risk. As mobile wallets or banking apps allow millions to access banking services without having to visit a traditional bank branch, these services are facilitating the financial inclusion. Data from mobile and online transactions can inform banks and lenders on a customer’s income and cash flow, acting as a risk indicator for formal loan applications. 

With a wealth of data available to FinTechs and financial institutions, it is now down to them to design solutions to process and analyse data which can inform loan eligibility. AI and Machine Learning models are proving crucial for this process with the ability to extract actionable insights from a wide range of unstructured data points drawn from these new data sources.  

Supporting a healthy global economy 

With the abundance of data available, its crucial for credit bureaus to work with FinTechs to enable wider access to finance for disadvantaged groups. On an individual level, having a strong credit score is crucial for many reasons but above all it enables individuals to secure housing, financing, utilities and even phone plans. Closing the gender lending gap will allow more women in particular to have access to capital to do all of these basic things as well as launch and grow businesses, creating jobs and boosting the local economy. 

Financial inclusion also goes beyond empowering individuals; it is the bedrock of a healthy economy. In today’s climate, with rising inflation and global economic uncertainty, countries need strong foundations to protect their economies. By expanding access to finance governments, banks and lenders can support and encourage local participation in the economy which in turn fuels broader and more resilient economic growth, benefiting entire regions and nations. 

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