By Ifeoma Nwakwesi Uddoh

Alternative credit data is becoming increasingly important in lending, offering a more inclusive way to assess financial trustworthiness. Traditional credit scores, introduced in the 1950s, primarily evaluate loan applicants based on credit card usage, loan repayment history, and outstanding debts. However, these models often overlook key financial behaviours, such as income consistency and essential bill payments, limiting access to credit for many individuals.

According to the World Bank, approximately 1.4 billion adults globally are unbanked, while an additional 3 billion have limited access to formal credit due to insufficient credit history. This exclusion disproportionately affects low-income individuals, young adults, and those in emerging markets. 

To address this gap, financial institutions are turning to alternative credit data—such as rent, utility, and mobile money transactions—to enhance risk assessment and expand lending opportunities. Despite these efforts, only 43% of lenders currently supplement credit scores with alternative data, highlighting the slow adoption of these models.

The Limitations of Traditional Credit Scoring

Traditional credit scores provide a snapshot of financial behaviour but often fail to account for the broader economic context of borrowers. Individuals who have never used a credit card or taken out a loan—sometimes referred to as “credit invisibles”—may struggle to secure financing, even if they have stable incomes and strong financial habits. 

In the U.S., about 26 million adults are classified as credit invisible, and another 19 million have insufficient credit history to generate a score, according to the Consumer Financial Protection Bureau (CFPB). In Africa, where about 400 million people lack access to formal financial services, traditional credit scoring systems have largely failed to support financial inclusion. Without access to credit, these individuals face difficulties in securing housing, investing in businesses, or managing emergencies, further widening the economic divide.

The Rise of Alternative Credit Models

Financial institutions are leveraging alternative credit data to gain a more comprehensive view of a borrower’s financial reliability. These models consider a broader range of indicators, such as rent payments, utility bills, mobile money transactions, and even behavioural spending patterns.

One of the most significant innovations in this space is open banking, which allows financial institutions to access real-time financial transaction data—with consumer consent—to assess creditworthiness. In the UK, open banking adoption has grown rapidly, with over 7 million users by mid-2023, according to the Open Banking Implementation Entity (OBIE). Fintech lenders are using this data to extend credit to individuals and small businesses that may not qualify under traditional criteria.

Artificial intelligence (AI) and machine learning are also playing a key role in credit assessment. These technologies analyse vast amounts of data, identifying patterns that traditional models might miss. For example, AI can assess income stability, savings behaviour, and even digital footprints—such as online subscriptions and e-commerce activity—to predict a borrower’s likelihood of repaying a loan. Companies like Upstart and ZestFinance in the U.S. use AI-driven underwriting models, reportedly approving 27% more borrowers than traditional methods while maintaining the same risk level.

Impact on Financial Equity

Alternative creditworthiness models have the potential to significantly improve financial inclusion, particularly in regions where traditional banking services are limited. Small business owners, freelancers, and gig workers—who often lack steady paychecks—can benefit from more flexible lending criteria. Digital lending platforms such as Branch, Tala, and M-Shwari use mobile transaction data to extend small loans to millions of borrowers who would otherwise be excluded from formal credit markets.

In Kenya, M-Shwari, operated by Safaricom and NCBA Bank, has disbursed over $4 billion in loans since its launch in 2012, with 40% of first-time borrowers using the platform to access credit for the first time. Similarly, in India, fintech companies like Paytm and KreditBee analyse digital transaction data to offer loans to previously unbanked populations, further demonstrating the potential of alternative credit models in emerging markets.

Challenges and Considerations

Data privacy is a major concern, as lenders collect sensitive financial and behavioural information. Without strong data protection regulations, borrowers may face potential misuse of their data. In the European Union, GDPR provides a framework for data security, but many developing economies lack similar safeguards, raising concerns about consumer protection.

Algorithmic bias is another issue. AI-driven credit assessments can inadvertently reinforce existing inequalities if the underlying data reflects systemic biases. For example, an AI model trained on historical lending data may inherit the same discriminatory patterns that excluded minority or low-income borrowers in the past. Ensuring fairness and transparency in these models requires continuous monitoring and regulatory oversight.

Moreover, while non-traditional data sources provide valuable insights, they may not always be reliable predictors of creditworthiness. A borrower may consistently pay rent and utility bills but still struggle with debt repayment due to unforeseen circumstances. Over-reliance on alternative data without proper validation mechanisms could lead to inaccurate credit assessments.