Alternative Credit Scoring: 5 Proven Ways Alternative Data Redefines Creditworthiness

TL;DR: Alternative credit scoring assesses creditworthiness using non-traditional data such as bank transactions, utility and rental payments, telecom records, e-wallet activity, and gig economy income. It makes thin-file and no-file borrowers visible to lenders, improves risk prediction, speeds up digital lending, and expands financial inclusion. As of the World Bank Global Findex Database 2025, 1.3 billion adults worldwide still lack a financial account. This guide explains how alternative credit scoring works, its 5 proven benefits, the key risks, and what implementation looks like for lenders.

Table of Contents

• What Is Alternative Credit Scoring?
• Why Traditional Credit Scoring Is No Longer Enough
• How Alternative Data Improves Creditworthiness Assessment
• Alternative Credit Scoring vs Traditional Credit Scoring: Side by Side
• 5 Ways Alternative Data Redefines Creditworthiness
• How Alternative Data Expands Financial Inclusion in Vietnam?
• What Alternative Credit Scoring Looks Like in Vietnam
• Challenges of Using Alternative Data
• Best Practices for Implementing Alternative Credit Scoring
• The Future of Creditworthiness Assessment in Vietnam
• FAQ

Introduction

Traditional credit scoring has long been the foundation of lending decisions. Banks, fintech companies, and financial institutions have typically relied on credit bureau records, repayment history, income documents, and existing loan performance to assess whether a borrower is creditworthy.

But this approach does not work for everyone.

In Vietnam, many individuals and small businesses remain difficult to assess through traditional credit models alone. First-time borrowers, informal workers, gig workers, micro-merchants, and SMEs may have real income, stable cash flow, and responsible financial behaviour, but limited formal credit history. Their financial activity is often fragmented across bank accounts, e-wallets, telecom networks, utility payments, business records, and digital platforms.

As a result, many creditworthy borrowers can be overlooked simply because they do not have enough conventional credit data.

This is where alternative credit scoring is changing the future of lending. By using alternative data such as bank transaction history, utility payments, rental payments, mobile wallet activity, telecom data, behavioural data, and device metadata, lenders can build a more complete view of a borrower’s financial behaviour.

For lenders, this creates an opportunity to make faster, smarter, and more inclusive credit decisions while improving risk assessment beyond traditional bureau-based models.

What Is Alternative Credit Scoring?

Alternative credit scoring is a method of assessing creditworthiness using non-traditional data sources. Unlike traditional credit scoring, which mainly depends on credit bureau data and past loan repayment records, alternative credit scoring looks at a broader range of financial and behavioural signals. These signals help lenders understand how a borrower manages money, pays bills, earns income, and interacts with digital financial services.

Common types of alternative data include:

• Bank account transaction history
• Cash flow and open banking data
• Utility bill payments
• Rental payment history
• Telecom and mobile phone payment data
• Mobile wallet and payment app activity
• Gig economy income
• Behavioural data
• Device metadata

When used responsibly, these data points give lenders a more accurate and real-time view of a borrower's financial reliability.

Why Is Traditional Credit Scoring No Longer Enough?

Traditional credit scoring works well for people with established credit histories. However, it often creates barriers for borrowers who are new to credit or underserved by formal financial systems.

The scale of the gap is large. According to the World Bank Global Findex Database 2025 (survey year 2024), 1.3 billion adults worldwide still do not have an account with a bank or regulated institution. Around 900 million of them own a mobile phone, which means their digital activity already produces data that alternative credit scoring can use.

A borrower may be financially responsible but still be rejected because they do not have enough credit history. A young professional applying for a first loan, a migrant worker, a freelancer, or a gig economy worker may not have a strong credit bureau profile. Without enough traditional data, lenders classify these borrowers as high-risk, and many potential customers are excluded from credit products even when they are capable of repayment.

Traditional credit scoring also relies heavily on historical information, so it may not reflect a borrower's current financial situation. Recent income, cash flow, payment behaviour, and digital financial activity provide valuable insights that traditional reports do not capture. Alternative credit scoring helps close this data gap.

How Does Alternative Data Improve Creditworthiness Assessment?

Alternative data provides a more holistic view of a borrower. Instead of only asking "Has this person borrowed before?", lenders can ask a better question: "Does this person show responsible financial behaviour today?"

For example, a borrower who consistently pays rent, utility bills, and phone bills on time demonstrates strong repayment discipline. A gig worker with regular income from digital platforms shows real earning capacity. These signals let lenders assess applicants more fairly and accurately.

Alternative credit scoring can also improve predictive accuracy. By combining traditional credit data with alternative data, lenders identify risk signals that do not appear in a standard credit report. This supports better loan approvals, more accurate pricing, and stronger portfolio management.

Alternative Credit Scoring vs Traditional Credit Scoring: Side by Side

The two approaches answer different questions about the same borrower. Traditional credit scoring asks what the borrower did with credit in the past. Alternative credit scoring asks how the borrower handles money right now. The table below summarises the differences.

5 Ways Alternative Data Redefines Creditworthiness

Alternative data is changing how banks, finance companies, and fintech lenders in Vietnam assess borrower creditworthiness. Instead of relying only on past loan repayment history, bureau records, or income documents, lenders can combine traditional credit data with real-world financial and behavioural signals, such as cash flow, payment history, digital transactions, telecom data, e-wallet activity, and platform-based business activity.

Importantly, alternative data does not replace traditional credit data. It complements it. When collected with customer consent, governed properly, and validated through robust model testing, alternative data can help lenders build a more complete view of repayment capacity, financial stability, and borrower reliability.

1. Identifying Creditworthy Borrowers Beyond Traditional Credit Data

Traditional credit scoring models typically assess borrowers based on formal credit history, existing loan performance, documented income, and previous repayment behaviour. This approach works well for borrowers with established credit records, but it can overlook individuals and small businesses that are financially active yet underrepresented in formal credit systems.

In Vietnam, this group may include first-time borrowers, freelancers, informal workers, gig workers, drivers on digital platforms, micro-merchants, household businesses, and SMEs. Many of these borrowers may have stable income, consistent cash flow, and responsible payment behaviour, but these signals are not always captured in traditional bureau data.

Alternative data helps lenders identify evidence of financial reliability beyond formal credit records. This allows credit teams to better recognise borrowers who may have the ability and willingness to repay, even if their conventional credit file is limited.

2. Assessing Repayment Capacity Through Real Cash Flow

Creditworthiness is not only about whether a borrower has repaid formal loans in the past. It is also about whether the borrower generates enough cash flow to meet future repayment obligations. This is especially important for freelancers, household businesses, micro-merchants, and SMEs, whose income may be real but not always reflected in payslips, employment contracts, or audited financial statements.

Alternative data can help lenders assess actual cash flow through signals such as transaction frequency, account inflows and outflows, bill payment history, e-wallet activity, platform-based revenue, and bank account activity. These signals can provide a more practical view of income generation, financial stability, and repayment capacity.

As a result, credit assessment becomes more grounded in current financial behaviour, especially for borrowers who are economically active but lack a strong formal credit history.

3. Adding Behavioural Signals to Credit Risk Assessment

Traditional credit data mainly shows how a borrower has used and repaid formal credit in the past. However, it may not fully capture current financial discipline, payment habits, or day-to-day financial behaviour, especially in a digital economy where people transact, pay, sell, and borrow across multiple channels.

Alternative data can add behavioural signals such as payment consistency, account stability, service usage patterns, spending trends, balance maintenance, on-time payment behaviour, and cash-flow regularity. When analysed responsibly, these signals help risk teams better understand a borrower’s financial discipline and reliability.

This shifts credit assessment from asking only “Has this customer borrowed before?” to also asking “How does this customer manage money today?”

4. Reducing Blind Rejections and Blind Approvals

A key limitation of traditional scoring models is that they can reject good borrowers simply because there is not enough credit history available. These are often thin-file or no-file borrowers. At the same time, a borrower with a strong credit history in the past may no longer have the same financial strength today if income, cash flow, or financial behaviour has changed.

Alternative data helps lenders reduce both types of decision errors. For borrowers with limited credit history, transaction data, payment behaviour, and digital activity can provide additional evidence of repayment capacity. For borrowers with existing credit files, more recent alternative signals can help detect changes in cash flow, financial stability, or risk behaviour.

This enables more balanced credit decisions: fewer good borrowers are rejected due to missing data, while fewer risky borrowers are approved based only on outdated historical information.

5. Enabling More Personalised Credit Decisions

When lenders have a richer view of a borrower’s cash flow, income patterns, spending behaviour, payment discipline, and financial stability, they can make more tailored credit decisions instead of applying the same standards to every applicant.

Alternative data can support more personalised loan amounts, repayment terms, payment frequencies, risk-based pricing, and product conditions. For example, two borrowers with limited credit history may present very different levels of risk when assessed through cash flow, payment behaviour, and income stability.

At a strategic level, alternative data helps creditworthiness move beyond a static score based mainly on past borrowing history. It enables a more dynamic, borrower-specific assessment of repayment capacity, financial behaviour, and product suitability.

How Alternative Data Expands Financial Inclusion in Vietnam?

Financial inclusion is one of the strongest arguments for alternative credit scoring. In many markets, people are excluded from formal credit not because they are irresponsible, but because the financial system does not have enough data about them.

By analysing everyday financial signals, lenders can recognise positive financial behaviour that traditional credit reports miss. This allows more people to become visible to the financial system. For lenders, this creates access to new customer segments. For borrowers, it creates opportunities to build financial identity, access credit, and participate more fully in the economy. In emerging markets, where cash incomes and informal work are common, alternative credit scoring is often the only practical way to bring first-time borrowers into the formal credit system at scale.

What Does Alternative Credit Scoring Look Like in Vietnam?

Vietnam is a natural market for alternative credit scoring. The National Credit Information Centre of Vietnam (CIC), the credit bureau operated under the State Bank of Vietnam (SBV), covers borrowers with formal credit relationships, but a large share of consumers and small businesses remain thin-file. At the same time, e-wallet usage, QR payments, and telecom data create rich alternative signals. DataCore already operationalises this with a Telco Alternative Credit Score service that turns consented network data from MobiFone and VNPT into lender-ready scores.

The same data gap affects business lending. Vietnamese small and medium enterprises (SMEs) routinely struggle to access bank credit because their financial footprint is fragmented across registries and filings, a problem we analysed in why Vietnamese SMEs cannot get credit and what structured company data fixes. Structured company data, such as DataCore's Company Intelligence Service, gives lenders verified registration, ownership, and operating signals that work like alternative data for SME credit decisions.

Consumer lenders in Vietnam also combine alternative credit scoring with digital identity verification. Fintechs are already re-wiring KYB onboarding around a single resolver call, and tools like DataCore's eKYC Service verify who the borrower is before any score is calculated.

Regulation matters here. Vietnam's Law on Personal Data Protection (Law No. 91/2025/QH15, the PDPL) took effect on 1 January 2026 and replaced Decree 13/2023/ND-CP. Any alternative credit scoring program in Vietnam must be built on explicit consent, purpose limitation, and impact assessments under the PDPL.

What Are the Challenges of Using Alternative Data?

Although alternative credit scoring offers many advantages, it also comes with important challenges.

Data Quality

Alternative data varies in accuracy, format, and reliability. Lenders must ensure that the data they use is complete, current, and trustworthy.

Privacy and Consent

Because alternative data may include personal or behavioural information, lenders must be transparent about what data is collected and how it is used. Customer consent and data protection are essential.

Regulatory Compliance

Credit decisions must comply with relevant lending, privacy, and consumer protection regulations. In Vietnam, that now means the PDPL; in other markets, frameworks such as the EU General Data Protection Regulation (GDPR) apply.

Explainability

Lenders need to explain how decisions are made. Explainability is critical for trust, compliance, and responsible lending.

Bias and Fairness

Alternative credit scoring should reduce exclusion, not create new forms of bias. Lenders need to test and monitor their models regularly to ensure fairness across different borrower groups.

Best Practices for Implementing Alternative Credit Scoring

To use alternative credit scoring effectively, lenders should follow a structured approach.

First, define clear objectives. For example, a lender may want to increase approval rates, serve thin-file customers, reduce defaults, or improve fraud detection.

Second, choose reliable data sources and technology partners. Data quality, compliance, security, and scalability should be key selection criteria. Our complete API guide to financial data in Vietnam covers how to evaluate providers.

Third, combine alternative data with traditional credit data where possible. The strongest models come from using both sources together rather than relying on one alone.

Fourth, keep models explainable. Risk teams, regulators, and customers all need to understand why an application was approved or declined.

Finally, continuously monitor model outcomes. Credit behaviour changes over time, so scoring models must be reviewed and improved regularly.

The Future of Creditworthiness Assessment in Vietnam

The future of creditworthiness assessment in Vietnam will be more data-driven, real-time, and inclusive. Traditional credit data will remain important, but lenders will increasingly combine it with alternative data to better understand a borrower’s current financial behaviour.

Instead of relying only on past loan repayment history, lenders can assess cash flow, payment behaviour, income stability, digital transactions, telecom signals, e-wallet activity, and business data. This helps make thin-file consumers, gig workers, informal workers, micro-merchants, and SMEs more visible to the financial system.

To be effective, this shift must be built on consent, data protection, explainability, and regular model monitoring. Lenders that adopt responsible alternative credit scoring will be better positioned to manage risk, serve modern borrowers, and expand financial access in Vietnam.

FAQ: Alternative Credit Scoring

What is alternative credit scoring in simple terms?

Alternative credit scoring is a way of judging whether someone can repay a loan using data outside the credit bureau file, such as bank transactions, utility bill payments, telecom payments, and e-wallet activity.

Is alternative credit scoring accurate?

Studies and lender experience show that combining alternative data with traditional bureau data improves predictive accuracy, especially for thin-file borrowers. Models still need regular validation and fairness monitoring.

Is alternative credit scoring legal in Vietnam?

Yes, provided the program complies with Vietnam's Law on Personal Data Protection (Law No. 91/2025/QH15, effective 1 January 2026), which requires explicit consent, purpose limitation, and data protection impact assessments.

What data does alternative credit scoring use?

Typical sources include bank account transactions, open banking and cash flow data, utility and rental payments, telecom and mobile top-up history, e-wallet and payment app activity, gig platform income, and non-sensitive behavioural and device signals.

Where can lenders get telco alternative credit scores in Vietnam?

DataCore provides a Telco Alternative Credit Score service based on consented network signals from MobiFone and VNPT, delivered via API alongside its Company Intelligence and eKYC services. Contact contact@datacore.vn for access.

Conclusion

Alternative credit scoring is redefining creditworthiness. By using alternative data, lenders can move beyond traditional credit history and assess borrowers based on how they actually manage money today.

For lenders, alternative credit scoring creates an opportunity to approve more customers while maintaining control over risk. For borrowers, it means that having little or no credit history does not have to limit access to financial opportunities.

Creditworthiness is no longer defined only by the past. With alternative data, it can be understood through real-time behaviour, financial responsibility, and a more complete view of the borrower. Lenders that build this capability now, with consent and explainability at the core, will set the standard that regulators and customers expect from everyone else.

Sources

• World Bank - The Global Findex Database 2025, July 2025
• Tilleke and Gibbins - Vietnam's New Personal Data Protection Law: A Closer Look, 2025
• Vietnam Briefing - Vietnam Law on Personal Data Protection: Overview, 2026