For decades, traditional credit scoring models have relied on a narrow set of financial data – primarily past loan repayments, credit card usage, and banking history. But in emerging markets, where millions lack access to formal financial services, these models leave many individuals and businesses without a reliable way to prove their creditworthiness.
This is where alternative data is transforming the lending landscape. By leveraging behavioural insights, mobile usage patterns, utility bill payments, and other non-traditional indicators, lenders can build a more comprehensive and inclusive picture of a borrower’s financial habits. This shift is enabling financial institutions to reach previously underserved populations while maintaining sound risk assessment practices. In many countries, this movement is being accelerated by Government-backed initiatives.
Alternative data refers to non-traditional sources of information that can help lenders assess credit risk. Some key examples include:
📱 Mobile and Digital Footprint – Payment history for phone bills, data usage patterns, and mobile money transactions can indicate financial discipline.
💡 Utility and Rent Payments – Consistently paying electricity, water, or rent bills on time can be a strong indicator of creditworthiness.
📊 Transactional Behaviour – Small but regular financial transactions, such as e-commerce activity, digital wallet usage, or ride-hailing payments, can show income stability and spending habits.
🧠 Gamified Psychometric Assessments – Short, interactive tests can measure traits like risk aversion, reliability, and conscientiousness to help build a borrower’s profile.
By incorporating these insights, lenders can go beyond the traditional “yes or no” approach and make smarter, more nuanced lending decisions.
One of the most promising forms of alternative data is character-based data, derived from psychometric assessments. These assessments provide first-party, opt-in data that measures psychological traits like responsibility, risk aversion, and financial discipline—all without requiring traditional personal or financial information.
Because psychometric assessments evaluate inherent behavioural traits rather than external financial histories, they offer a fairer and more accurate measure of a borrower’s true risk profile. Furthermore, as these assessments do not rely on sensitive financial details, they align with global data protection standards such as GDPR and other emerging privacy regulations, ensuring compliance while still providing actionable credit insights.
Modern psychometric assessments are a long way from the multiple-choice questionnaires of the past. Begini offers a gamified character assessment based on psychometrics that ensures high completion rates and favourable user experience. By using games and tasks – not questions – we assess people on how they think and learn, not what they tell us about themselves. Don’t ask people about their behaviours, allow them to show you, that is the power of modern character assessments.
The rise of alternative data-driven credit scoring isn’t just about inclusivity—it’s also good business, benefiting both lenders and borrowers:
📌 Case Study: Asset Financing Across Africa A not-for-profit organization operating in Malawi, Zimbabwe, Ghana, and Rwanda, sought a way to assess the lending risk of individuals repaying loans for digital devices. By integrating Begini’s psychometric assessment into their loan application process, they increased loan approvals by 300% while also improving non-performing loan (NPL) forecasts. This helped refine product offerings dynamically across different markets.
📌 Case Study: Empowering Caribbean SMEs A web-based lender in Trinidad and Tobago needed a way to assess small businesses beyond traditional credit analysis. With Begini’s psychometric solutions, they gained deeper insights into entrepreneurs’ willingness to repay. This enriched data strengthened their scoring models, allowing them to confidently approve more loans and expand into Barbados and other Caribbean markets.
📌 Case Study: BNPL Lending in Thailand A major Thai bank wanted to expand its buy-now-pay-later (BNPL) offerings without increasing risk. They implemented Begini’s psychometric assessments on over 100,000 applicants, successfully categorizing risk levels. The results showed that borrowers scoring above 80 were 3x less likely to default than those scoring below 50, proving the effectiveness of alternative data in lending decisions.
📌 Case Study: Financing for the Informal Economy in Honduras The leading retailer in Honduras integrated Begini’s psychometric assessment into its financing processes to extend credit to unbanked populations. The solution had a 96.4% completion rate and a 23.3% rescue rate, meaning nearly a quarter of previously rejected applicants were reconsidered. This initiative significantly improved financial inclusion while maintaining a strong loan portfolio.
📌 Case Study: Transforming Homeownership in Colombia A proptech startup in Colombia had a gool to enable homeownership through a rent-to-own model. By leveraging Begini’s alternative data solutions, they launched character-based credit assessments in just one day, identifying key behavioural traits linked to repayment commitment. These insights are helping underserved individuals build their creditworthiness and achieve homeownership more efficiently.
📌 Case Study: Supporting Small Businesses in Latin America A fintech firm in Latin America used psychometric assessments combined with digital wallet transactions to evaluate small business owners. This approach helped them approve 40% more applications and offer tailored loan products to growing enterprises.
As the financial landscape evolves, some governments are actively encouraging lenders to consider alternative data sources to improve credit access. Regulatory bodies in countries such as India, Brazil, and Kenya have introduced frameworks that allow mobile payments, utility bills, and psychometric assessments to be included in credit scoring models. These initiatives aim to expand financial inclusion while ensuring responsible lending practices.
For example, the Reserve Bank of India (RBI) has promoted digital lending guidelines that support the use of alternative data to assess borrowers who lack traditional credit histories. Similarly, Brazil’s central bank has introduced open banking policies that allow financial institutions to access consumer-permissioned alternative data, enabling more accurate risk assessment for underserved populations.
By integrating alternative data, lenders not only comply with evolving regulatory standards but also unlock new opportunities to reach untapped markets.
By embracing alternative data, lenders can open new revenue streams while promoting financial inclusion. Borrowers, in turn, gain access to fairer, more personalized financial services. As technology continues to evolve, alternative data will play a crucial role in shaping the future of credit scoring – one that is more equitable, efficient, and impactful.
Alternative data expands credit access by analysing behavioural insights, mobile transactions, utility payments, and character data. This helps lenders assess financial responsibility even when traditional credit data is unavailable.
Yes, studies show that behavioural and character data from sources such as psychometric assessments can accurately predict loan repayment rates. Additionally, as there is little correlation between alternative and traditional credit data, they are can be combined to provide predictive uplift.
Yes. Many alternative data sources, such as psychometric assessments, operate on first-party, opt-in data without requiring personal financial information. This ensures compliance with GDPR and other local privacy laws while maintaining strong predictive power.
Absolutely. By analysing real-time behavioural patterns, lenders can distinguish between high- and low-risk borrowers more effectively. Case studies have shown up to 6% reductions in non-performing loans (NPLs) using alternative data models.
Alternative data is particularly useful for microfinance institutions, digital lenders, BNPL providers, and banks looking to expand credit access in emerging markets. It helps reach underbanked populations, small businesses, and gig economy workers who may lack formal credit histories.
SaaS technology that provides alternative data credit scores for Banks, Micro Finance, Digital Lenders, Neo Banks, BNPL and Asset Financing.
