AI-Driven Loan Approvals: How New Risk Models Are Giving Informal Workers Better Access to Credit

South Africa has one of the largest informal sectors in Africa. Millions of people work as:

• hairdressers, barbers, and nail technicians 
• street vendors 
• spaza shop owners 
• domestic workers 
• gardeners 
• construction freelancers 
• taxi drivers 
• delivery riders 
• gig workers (Uber, Bolt, MrD, Takealot) 
• artisans and repair technicians 
• casual retail workers 

These workers generate billions of rands each year — yet most of them cannot access formal credit.

Why?

Because traditional banks require:

• payslips, 
• bank statements, 
• formal employment history, 
• stable monthly income, 
• high credit scores, 
• predictable cashflow. 

Informal workers rarely meet these requirements. As a result, they are labelled “thin-file customers” — customers with little or no verifiable credit data.

But in 2025, a fundamental shift is happening.

A new wave of fintech lenders and digital banks is using AI-driven risk models to evaluate borrowers based on behaviour, transaction history, gig income patterns, and digital footprint — not just payslips.

This technology is beginning to unlock credit access for people who were historically invisible to the financial system.

This article explains:

• how AI-based loan models work, 
• why they are more inclusive, 
• which data sources they use, 
• why they matter for informal workers, 
• risks and limitations, 
• and how this trend could reshape lending in South Africa. 

1. The Big Problem: Traditional Credit Models Exclude Millions

Traditional lending relies heavily on:

1. Payslips

Proof of stable monthly salary.

2. Employment contracts

Preferably long-term.

3. Bank statements with predictable deposits

Monthly, consistent amounts.

4. Credit scores

Dependent on previous borrowing.

5. Registered employers

Formal businesses with HR departments.

But the reality is:

• Over 30% of South Africa’s workforce is informal. 
• Over 40% of people aged 18–35 have no credit history. 
• Gig workers can earn well — but irregularly. 
• Domestic workers are paid in cash or EFTs that look “unstable”. 

The result?

Millions of financially responsible people are treated like high-risk borrowers simply because they don’t fit traditional banking boxes.

2. What Are AI-Driven Loan Models?

AI-driven lending models use machine learning to assess creditworthiness based on behavioural and transactional data rather than rigid paperwork.

The system studies patterns, not documents.

Instead of asking:

“Does this person have a stable employer?”

AI asks:
“Does this person show reliable financial behaviour over time?”

AI evaluates hundreds of variables instantly — far more than any human loan officer could.

3. The Types of Alternative Data AI Uses to Evaluate Informal Workers

AI models pull data from a wide set of digital footprints, including:

1. Mobile Money Transactions

• airtime purchases 
• electricity top-ups 
• mobile wallet transfers 
• prepaid data usage 

Patterns reveal income regularity and spending habits.

2. Bank Transaction Behaviour

Even if irregular, AI spots patterns:

• repeat deposits from clients 
• cashflow cycles 
• monthly bill payments 
• seasonality (e.g., December spike) 
• average monthly earnings 

3. Gig Platform Income

For workers on:

• Uber 
• Bolt 
• MrD 
• Takealot 
• SweepSouth 
• Fiverr 
• Upwork 

AI reads:

• weekly earnings 
• customer ratings 
• job frequency 
• cancellation rates 
• delivery trends 

4. Merchant Payments

For informal traders:

• consistent sales 
• POS device usage (Yoco, iKhokha) 
• QR payments 
• cash deposit patterns 

5. Digital Wallet Data

Apps like:

• TymeBank 
• Capitec 
• eWallet 
• Shoprite Money Market 
• PayShap 
• Ozow 

provide additional financial signals.

6. Utility Payment History

Paying:

• electricity 
• water 
• rent
  on time shows reliability. 

7. Behavioural Biometrics

AI tracks:

• typing rhythm 
• app usage patterns 
• login consistency 
• location stability 

This helps detect fraud and verify identity.

8. Phone Metadata

Not content — but patterns like:

• how long the SIM has been active 
• contact list stability 
• device age 
• mobile bill payment consistency 

These are strong predictors of financial reliability.

9. Social Proof Signals

This DOES NOT mean reading private messages.
Instead, AI looks at:

• business pages 
• delivery ratings 
• marketplace reputation (Takealot Sellers, Facebook Marketplace traders) 

All of these signals help replace the need for formal documentation.

4. Why AI-Based Lending Is More Inclusive

1. Income doesn’t need to be stable — just consistent enough

AI can detect cashflow patterns even if “messy”.

2. No payslip required

This alone opens doors for millions.

3. Cash earners can be evaluated

If they deposit or spend digitally, AI sees it.

4. Higher approval rates for gig workers

AI understands earnings fluctuations.

5. Better risk segmentation

Good borrowers are no longer punished for informal income.

6. Faster approvals

AI takes seconds, not days.

7. Fairer decisions

Models use behaviour, not prejudice.

This creates a more equitable financial environment.

5. Examples of AI Lenders Helping Informal Workers (Global + Local Trends)

1. Tala (Kenya + Philippines)

Uses mobile data + behavioural patterns.

2. Branch (Africa + India)

Approves loans using smartphone analytics.

3. FairMoney (Nigeria)

Evaluates gig workers and small business owners.

4. TymeBank (South Africa)

Uses advanced behavioural scoring.

5. Capitec (South Africa)

Leverages transaction modelling for thin-file customers.

6. Stitch + Lipa Payments

Provide data rails for alternative scoring.

7. Yoco Capital

Offers loans to small business owners using POS transaction history — no paperwork.

8. Ozow + Payflex partners

Use real-time bank data to evaluate risk.

SA is early, but adoption is accelerating.

6. Why AI Risk Models Are Perfect for South Africa

1. Huge informal economy

AI understands irregular income better than traditional methods.

2. High smartphone penetration

Over 85% of adults use smartphones — ideal for alternative data.

3. Growing digital payments

QR codes, tap-to-pay, and mobile money generate usable data.

4. Government push for financial inclusion

AI helps reach unbanked and underbanked populations.

5. Rising gig economy

As gig work expands, traditional models become obsolete.

6. Declining trust in traditional banks

Fintechs fill the gap with more flexible models.

7. What Loans Look Like Under AI-Based Systems

Loan amounts

R300 – R50,000 depending on income patterns.

Repayments

Flexible:

• weekly 
• monthly 
• per-delivery (for gig workers) 

Interest

Varies by risk — AI lowers rates for good behaviour.

Approval time

30 seconds to 5 minutes.

Collateral

Not required.

Paperwork

None.

8. Realistic Example: How AI Approves an Informal Worker

Let’s imagine Zanele, a mobile hairdresser in Soweto.

She earns:

• R200–R500 per day 
• deposits cash periodically 
• receives WhatsApp bookings 
• uses her card to buy hair supplies 

Under traditional models:
❌ No payslip
❌ Irregular income
❌ No credit history
= Rejected

Under AI models:
✔ AI sees repeating patterns in her deposits
✔ Notices purchases at hair supply stores
✔ Finds stable SIM history
✔ Identifies consistent mobility and business activity
✔ Detects on-time electricity payments
✔ Creditworthiness scored fairly

Zanele gets a R1,500–R5,000 loan — responsibly priced.

9. Benefits of AI Lending for Informal Workers

1. Credit becomes accessible

Loans for:

• stock 
• tools 
• emergencies 
• transportation 
• school fees 
• electricity during load-shedding 

2. Fairer interest rates

No more being grouped with high-risk borrowers.

3. Faster approvals

Minutes, not days.

4. No paperwork stress

No HR documents required.

5. Better financial security

Can smooth income fluctuations.

6. Path to formal credit

Small loans → good behaviour → larger loans.

10. Risks and Challenges of AI Lending

1. Algorithmic bias

AI may inherit biases from flawed data.

2. Privacy concerns

Data must be handled ethically.

3. Overborrowing

Easy access increases temptation.

4. Regulatory gaps

The NCR must update rules to govern AI lending.

5. Fraud tactics evolving

Criminals try to “game” algorithms.

6. Digital exclusion

People without smartphones miss out.

AI lending must be combined with financial education and fair regulation.

11. Will AI Replace Traditional Credit Scoring in SA?

Not fully — but it will dominate new lending.

AI WILL replace:

• small loans 
• gig worker loans 
• informal worker loans 
• payday alternatives 
• micro-loans 
• stock loans for small traders 
• side-hustle loans 

AI WILL NOT replace:

• mortgage finance 
• car loans (not yet) 
• large business loans 

AI will augment, not destroy, traditional systems.

Conclusion: AI Lending Is Opening the Credit Door for Millions in SA

AI-driven lending is one of the most significant financial shifts of the decade in South Africa.
It gives informal workers something they have lacked for years:

• visibility 
• fairness 
• access 
• dignity 
• opportunity 

Instead of being judged for not having a payslip, consumers are now evaluated based on how they actually live, earn, and manage money.

AI is not perfect — but when used responsibly, it can unlock credit for millions who have been excluded for far too long.

This is not the future.
This is happening right now.

 

We hope this information has been very useful to you.

Thank you very much for reading us.

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