Fintech Innovations That Are Used To Score Your Credit

Many people still fall outside the reach of traditional credit scoring. In the United States, the Consumer Financial Protection Bureau counts about 26 million adults who are “credit-invisible” and another 19 million whose files are too thin for a score. South Africa presents the reverse image: roughly 18.5 million consumers are active credit users, yet a large share holds only one or two small accounts that do little to build a full record.

Fintech companies in both countries are trying new ways to measure risk—using mobile wallet history, utility payments, and other data that banks once ignored. This shift also explains why some fast, digital loans may leave no trace on your credit report: the lender keeps the information in-house unless a serious default occurs. That privacy can feel like an advantage, but also means on-time payments will not boost your score. The following article examines how innovators balance these trade-offs and what it could mean for your next loan application. The article below discusses the key fintech tools driving innovation and what this could mean for your next loan application.

In South Africa, traditional credit scores have long been based on bank statements and store accounts. This approach worked for the 28 million people with a credit history. However, about 7 million adults are still outside the system — they do not have a bank account or a formal job.

Fintech companies have started to change the rules of the game. Instead of standard data, they use information about mobile payments, geolocation, online purchases, and behavioral habits. This helps to assess risks and give a chance for credit to those who do not have a credit history. Such borrowers get access to small loans and gradually build their credit scores.

The situation is different in the US: the FICO score has been around since 1989 and covers 90% of the population. Only 4.5% of American households are unbanked. But even there, new solutions are emerging: Experian Boost, UltraFICO, and FICO Score XD take into account data on utility payments and banking transactions and allow for an additional assessment of 26 million clients “invisible” to creditors.

Why the trajectories differ:

• Market maturity. The USA’s long-standing bureau infrastructure left a narrower “missing-file” population than in South Africa, where formal credit only became widespread after 1994.
• Data sources. South African inclusion efforts lean on mobile-money ecosystems (nearly universal SIM penetration), whereas US projects leverage open-banking APIs and widespread utility/telecom autopay adoption.
• The scale of innovation. Both countries now permit consumer-permissioned data, but US lenders typically integrate it into existing bureau scores, while South African fintechs often run stand-alone models for first-time borrowers.

Traditional Credit Bureaus vs. Fintech Credit Risk Tools

Traditional credit bureaus like Experian South Africa still dominate South African credit scoring. The institution collects data from banks, retail credit providers, and utility companies. It generates a score based on repayment history, account age, debt levels, and inquiries.

The scores are suitable for several users yet fail to reach everyone. The lack of formal credit records combined with rare bank transactions leads to low or absent score assessments even though people show financial responsibility through different channels.

Fintech tools serve as risk evaluators through their alternative data analysis capabilities. When users get approval, the scoring system evaluates user behavior through mobile wallets, prepaid electricity payments, and social media interactions. Alternative data tools can locate hidden patterns that standard screening systems fail to detect, especially for hidden workers within informal sectors and new entrants in the job market.

A similar tension exists in the U.S., where the “big three” bureaus (Experian, Equifax, TransUnion) still anchor underwriting. Yet, federal proposals under the Fair Credit Reporting Act now press them to incorporate rent, BNPL, and subscription data to stay competitive with fintech models.

The Role of Credit Scoring Fintech in Financial Inclusion

One of the biggest benefits of credit scoring fintech is its impact on financial inclusion. Millions of South Africans are considered “credit invisible.” They don’t have enough formal credit history to get a traditional score, which locks them out of loans, property, and even some employment opportunities.

Fintech solutions bridge this gap by offering “alternative credit scoring.” This approach uses non-traditional data to measure financial behavior. If someone regularly pays their phone bill, buys electricity through a mobile app, or receives regular mobile money transfers, that can show responsibility, even if they’ve never had a bank loan.

Startups and banks using these systems can offer low-value loans, BNPL services, and overdraft products to users who would otherwise be rejected. This opens up new paths to build credit and grow financially.

In pilot programs such as Colorado’s 2024 Rent Reporting for Credit initiative, U.S. tenants who added rent history to their credit files saw average score increases of 80 points, illustrating comparable inclusion gains.

Several fintech companies are leading the way in reshaping credit scoring in South Africa. Each has its own model and focuses on different types of data.

Yoco’s Merchant Insights and Small Business Data

Yoco is known for its point-of-sale devices for small businesses. Behind the scenes, it collects detailed transaction data on sales, returns, customer volume, and cash flow. This data helps assess a business’s health, even if the owner has no personal credit score. Yoco uses this information to offer cash advances to merchants. It’s a simple, data-driven way to fund informal and micro enterprises that would never qualify through a bank.

JUMO’s Alternative Credit Scoring Platform

JUMO is a platform that partners with mobile network operators and banks to offer financial services across Africa. It collects data like airtime top-ups, mobile money activity, and phone usage to build a credit profile.

Lenders can offer loans through mobile phones, even to users without bank accounts. JUMO has already served over 20 million people, many of them first-time borrowers.

Lulalend’s Real-Time Credit Decision Engine

Lulalend uses automated systems to assess business loan applications. It combines traditional credit checks with real-time business performance data, such as sales volume, revenue consistency, and digital footprint. Lulalend’s model allows it to approve loans in minutes, making it ideal for small and growing businesses.

Tala’s Mobile-Based Credit Analysis

Tala operates a mobile app that scans a user’s phone (with permission) to analyze behavior. To estimate risk, it looks at text messages, app usage, call history, and bill payments.

This creates a detailed, real-time credit profile, allowing Tala to offer loans to people who have never interacted with a bank.

PayJustNow’s BNPL Model and Credit Profiling

PayJustNow is a South African “buy now, pay later” service. It uses AI to assess risk at checkout based on user behavior and transaction patterns. Customers can split payments over time without interest. Behind the scenes, PayJustNow is building rich consumer data profiles that can support future credit products.

TymeBank’s Customer Behavior Analytics

TymeBank is a digital bank with over 8 million customers. It uses machine learning to analyze customer activity, including spending patterns, savings behavior, and transaction history. This helps TymeBank offer personalized products and assess credit risk even among users with no formal credit background.

How Alternative Scoring Models Work in Fintech Credit Risk

Alternative scoring models rely on data that is usually ignored by traditional systems. This could include:

• Mobile phone usage
• eWallet and mobile money transactions
• Utility and prepaid electricity purchases
• Social network behavior
• App download and usage history
• Location and device stability

These models use algorithms to find patterns. For example, someone who regularly tops up their phone and pays utility bills on time is likely to repay a loan, too. Some models also look at how long someone has had a SIM card or how regularly they use the same device, which can signal stability.

In the United States, FICO XD cash-flow analytics perform similar pattern matching, inserting on-time phone and streaming service payments directly into bureau files to recalibrate risk.

Regulatory Landscape and Data Privacy Concerns

As these tools grow in popularity, regulation is catching up. South Africa’s Protection of Personal Information Act (POPIA) governs how companies collect and use data. Fintech firms must get informed consent before accessing sensitive data like phone usage or location.

The National Credit Regulator (NCR) also plays a role. It ensures lenders use fair practices and don’t discriminate based on faulty or biased models.

One major concern is transparency. Consumers often don’t know what data is used to score them or how their score is calculated. More fintech companies show users their credit data and explain how decisions are made.

Equivalent consumer protections in the U.S. derive from the Fair Credit Reporting Act, the Equal Credit Opportunity Act, and recent CFPB proposals to curb opaque data-broker practices, signaling a shared regulatory push toward algorithmic transparency.

Benefits of Using a Fintech-Driven Credit Score

Here are the advantages of new credit score systems:

• Faster approvals. Many tools provide instant decisions.
• More inclusion. People without a traditional credit history can still qualify.
• Better risk models. Multiple data sources reduce the chance of error.
• Lower costs. Automation and digital tools reduce paperwork and admin.
• Lenders can grow faster and reach more customers.

U.S. studies of AI-driven lenders show approval rates rising by more than 40 percent with 36 percent lower APRs, illustrating parallel efficiency gains.

Challenges with Fintech Credit Scoring Models

While promising, there are still challenges:

• Data quality. Some sources are unreliable or incomplete.
• Bias and discrimination. Algorithms can reflect existing social or economic biases if not carefully managed.
• Privacy concerns. Users may not fully understand what data they’re sharing.
• Regulatory uncertainty. New models may not fit neatly into current rules.

Future Outlook for Alternative Credit Scoring Fintech Solutions

South Africa’s financial system will experience rising Fintech involvement. Digital payment adoption by South African citizens and their transition to online services will generate progressively usable data. More intelligent solutions will improve the availability of accessible scoring models.

Traditional lenders and banks implement these tools by establishing partnerships and acquisitions with various financial technology companies. Technology companies and financial institutions merge to create a single domain between fintech and finance services.

Future developments in fin-tech will merge services while enhancing user visibility through improved model sophistication to deliver better results to consumers and small enterprises.

A comparable trajectory is unfolding in the United States, where open-banking rules expected in 2025 aim to expand consumer-permissioned data sharing and accelerate lender–fintech collaboration.

Final Thoughts on Adopting Fintech Credit Tools

Using credit scoring technologies from fintech companies revolutionizes South Africans’ approaches to getting financial services. Alternative data alongside modern technology has unlocked a more effective credit system that provides bigger and speedier access to customers with more precise metrics.

Two parties within South African financial markets benefit from fintech credit tools because they help lenders access untapped markets while consumers build their credit profiles. They demonstrate financial power for growth and inclusion if their operations maintain transparency, fairness, and security standards.

Ultimately, South Africa and the United States are converging on a hybrid model where traditional bureaus, alternative data, and AI-driven analytics coexist—each country learning from the other’s successes and regulatory safeguards to broaden responsible access to credit.