Data and AI-driven credit in emerging economies

How money moves using alternative data for decision-making

Written by
Cho-Nan Tsai

Editor’s note

The credit gap for small, informal or individually-owned businesses in emerging markets is only increasing, despite the fact that access to initial lines of credit has been proven to significantly alter the chances for a small business to stand on its feet and become a significant source of income for its founders. One major reason for this is that traditional credit facilities still rely on antiquated means of credit scoring to determine eligibility. However, there's one thing that continues to grow richer all across the world - data. Here we explore the ways in which AI can be leveraged to learn from available mobile phone, social media and other data, creating new ways to predict an entrepreneur's ability to pay back credit, therefore unlocking access to critical growth capital.

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The credit gap in emerging economies

Solopreneurs in emerging economies face a distinctive set of challenges when it comes to accessing credit. Unlike established businesses with extensive financial histories, these enterprising individuals often lack the collateral, credit history or formal documentation that traditional lenders typically require. Their businesses are characterized by informality, making it difficult to assess their creditworthiness through conventional means.

Credit plays a pivotal role in the growth and sustainability of solopreneurial, informal or small ventures. For these business owners, it serves as the lifeblood that enables them to invest in new equipment, expand their product lines, hire additional help or simply navigate through lean times. Without access to credit, their businesses may remain stagnant or struggle to survive, limiting their potential for innovation and economic impact.

The credit gap in emerging economies is a stark reality that affects countless solopreneurs and SMEs globally. According to the World Bank, a significant percentage of small and micro-businesses in emerging economies lack access to formal credit, and the gap remains at $2-2.5 trillion and counting. For example, in countries like Mexico, Brazil or India, a substantial portion of the entrepreneurial population operates in the informal sector, where financial exclusion is rampant. 

Here, we delve into how data and artificial intelligence are beginning to address these challenges by revolutionizing the lending landscape and providing solopreneurs with the credit opportunities they deserve.

The evolution of traditional vs. data-driven lending

Traditional lending institutions have long been the go-to source for credit, relying on conventional practices such as assessing applicants based on credit scores, collateral and income verification. While these methods have served as benchmarks for assessing creditworthiness, they come with limitations, particularly for solopreneurs in emerging economies. Traditional lenders often struggle to evaluate the potential of businesses that don't have extensive financial histories or tangible assets. This can result in the exclusion of many deserving entrepreneurs from the credit market.

In contrast, the emergence and proliferation of data-driven lending over the past few years represents a paradigm shift in the way credit decisions are made. Data-driven lending leverages technology, big data and artificial intelligence to assess credit risk. Rather than solely relying on traditional metrics, data-driven lenders consider a broader range of data sources, including transactional data or payment history, online behavior and social media activity. These alternative sources provide a more comprehensive and nuanced view of an applicant's creditworthiness, enabling lenders to make more informed decisions.

Data-driven lending offers several advantages for solopreneurs in emerging economies. Firstly, it opens up access to credit for those who would otherwise be excluded due to limited credit history or collateral. By analyzing a diverse set of data points, lenders can identify businesses with strong growth potential, allowing solopreneurs to secure the financing they need to expand and thrive. Additionally, data-driven lending processes are often quicker and more efficient, reducing the time and effort required to access funds, which can be crucial for small businesses with immediate needs. Lastly, the flexibility of data-driven lending models can result in more personalized loan terms, better aligning with the unique circumstances and requirements of solopreneurs.

The role of data in assessing creditworthiness

To evaluate a borrower's creditworthiness, data-driven lenders employ a multifaceted approach. They gather and analyze a wealth of information to form a comprehensive picture of the applicant's financial health and reliability as a borrower. Unlike traditional lenders, data-driven lenders are not solely fixated on credit scores and collateral. Instead, they take into account a broader range of data, including:

  1. Financial Data
    This includes traditional financial records, such as income statements, tax returns and bank statements. While these sources are familiar, data-driven lenders may employ advanced algorithms to scrutinize them more deeply, identifying patterns and trends that may not be evident through conventional assessment methods.
  2. Personal Data
    Personal information about the applicant, such as age, education and employment history, can provide insights into their stability and commitment to repaying loans.
  3. Transactional Data
    One of the key differentiators in data-driven lending is the use of transactional data. This comprises data from financial transactions, both business and personal, which can reveal spending habits, income streams and financial responsibility. Transactional data is often collected through digital payment platforms, allowing lenders to gain real-time insights.

The importance of alternative data sources

For solopreneurs in emerging economies who may lack a traditional credit history, alternative data sources become invaluable. These sources can include:

  1. Mobile phone usage
    Insights from mobile phone usage patterns, such as call history and mobile money transactions, can provide valuable information about a borrower's financial activity and reliability.
  2. Online behavior
    Data-driven lenders may analyze an applicant's online behavior, including social media activity, to gauge their credibility and stability.
  3. E-commerce transactions
    For businesses engaged in e-commerce, transaction data from online sales can be a powerful indicator of business performance and revenue.
  4. Utility payments
    Consistent utility payments, such as electricity or water bills, can be used as evidence of financial responsibility.

By harnessing these alternative data sources, data-driven lenders can extend credit to solopreneurs who may have previously been excluded from traditional lending due to a lack of credit history. This approach not only enhances financial inclusion but also fosters economic growth by empowering entrepreneurs to access the capital they need to expand their businesses.

Leveraging Artificial Intelligence for credit scoring

In the ever-evolving landscape of lending, the most successful models don't always rely on traditional formal credit data from credit bureaus. Instead, they look beyond the conventional metrics and turn to an individual's device data and digital footprint as valuable sources of information. This approach, powered by artificial intelligence (AI), has paved the way for a more accurate and inclusive credit assessment process.

AI algorithms can process vast amounts of data swiftly and efficiently, making it possible to consider a wider range of factors when evaluating creditworthiness. When it comes to assessing solopreneurs and individuals with limited credit history, AI-driven models can help with:

  1. Data variety
    AI can analyze a diverse set of data sources, including device data (such as smartphone usage patterns), digital footprints (online behavior and interactions), transactional data and even social media activity. This wealth of data offers a more comprehensive view of an applicant's financial behavior and reliability.
  2. Pattern recognition
    AI excels at recognizing patterns and trends in data that might be missed by traditional credit scoring models. It can identify subtle indicators of creditworthiness, such as consistent payment behavior, responsible financial management and a reliable income stream.

Benefits of AI in lending decisions

The integration of AI in lending decisions can aid in:

  1. Accuracy
    AI-driven credit scoring models are often more accurate in assessing credit risk. By considering a broader set of data, they reduce the reliance on traditional credit scores, which may not accurately reflect an applicant's true financial situation.
  2. Inclusivity
    AI-driven models allow solopreneurs and individuals with limited formal credit histories to access credit based on their actual financial behavior rather than historical data.
  3. Efficiency
    AI streamlines the lending process, enabling faster and more efficient decision-making. This is particularly crucial for small businesses and entrepreneurs who often require quick access to funds.

Case studies and success stories

Case 1: Camino Financial and Luna.AI - revolutionizing Latinx business intelligence

Online lender Camino Financial created Luna.AI, an AI-driven Latinx Business Intelligence platform. Luna.AI set out to compile the largest proprietary dataset on the Latinx market, through artificial intelligence and behavioral data. With its headquarters in Los Angeles and operational offices in Mexico City and Bogota, Camino Financial aimed to make agile lending decisions and provide funding to the unbanked. To date, they have deployed a staggering $183 million in national capital.

By leveraging AI-driven insights and behavioral data, Camino Financial was able to bridge the credit gap for Latinx entrepreneurs, offering them the financial support they needed to grow their businesses. This pioneering approach not only demonstrated the potential of AI-driven lending but also showcased the impact it could have on underserved communities in emerging economies.

Case 2: - lightning-fast point of sale financing for consumers in Egypt

In the vibrant fintech landscape of 2021, Blnk emerged as a game-changer in Egypt's lending ecosystem. This is an example of how AI and data-driven lending practices can also extend beyond solopreneurs to individuals, enabling loans at point of sale even for those without credit history. Boosk's digital lending platform partnered with local merchants, enabling them to underwrite customers at the point of sale and provide instant financing for a range of products, from electronics to furniture and automotive services. What set apart was that it could offer credit to customers in under three minutes, from the initial application to the final approval.

By simplifying and expediting the lending process, empowered Egyptian consumers with quick access to financing, enhancing their purchasing power and driving economic activity. This demonstrated how technology could revolutionize access to credit, especially in emerging economies, where speed and convenience are paramount.

Case 3: Transforming physical retail banking into digital success in Southeast Asia

In Southeast Asia, a forward-thinking bank faced a challenge – it had acquired retail banking branches in Thailand, Vietnam and the Philippines, but its retail banking operations were underperforming. The leadership recognized the need for a radical transformation: converting these physical retail branches into thriving digital retail banking hubs.

This endeavor required a strategic shift in approach and a deep understanding of the local markets. By embracing digital banking solutions and tailoring them to the unique needs of customers in each country, the bank aimed to turn the tide and transform underperforming branches into profitable digital banking hubs. This case exemplifies the adaptability and resilience required to navigate the changing landscape of banking in emerging economies, where innovation is key to success.

Challenges and risks

While AI-driven credit scoring holds immense promise, it’s not without challenges and ethical considerations. Some include:


Data and artificial intelligence have a transformative role to play in the realm of credit access for solopreneurs in emerging economies.  Access to credit provides a lifeline to entrepreneurial dreams and economic growth, making it imperative that we stay informed about the evolving landscape of data and AI-driven lending. The key to success lies in embracing the possibilities and remaining vigilant for the new developments that will continue to shape the world of solopreneurs in emerging economies, opening doors to opportunities that were previously out of reach.

About the author

Cho-Nan Tsai

Cho-Nan Tsai is a seasoned technology entrepreneur in AI and Fintech and an expert in AI, machine learning and lending. Earlier in his career, he managed tech projects at Fortune 500 companies such as Sony, Ricoh, McKinsey & Company. More recently, he bootstrapped and raised millions of dollars for a number of startups. Currently, he is the founder at H Tech VIP, an AI consulting firm providing services in technology, consulting and advisory at the intersection between AI and Fintech, particularly for risk management, borrower targeting and collection.