Beyond Traditional Credit Scores: How Payment Gateway Data Is Transforming SME Lending

Traditional credit models in MENA struggle with “thin-file” SMEs and startups. Many small businesses lack lengthy bank histories or collateral, so credit bureaus cover only a fraction of the market. In fact, only about half of MENA’s adults have financial accounts, and SMEs receive a tiny share of formal lending (roughly 8% of MENA’s total credit, just 2% in the GCC). As a result, dozens of millions of creditworthy enterprises remain excluded. Underwriters note that rigid bank requirements and manual processes further delay or prevent loans for agile digital firms. In short, legacy scores omit a huge pool of young or digital-native SMEs. This gap has spurred lenders to seek new signals from merchants’ own data.

Alternative Data Signals from Gateways and POS

Payment gateways and point-of-sale systems now generate rich, behavioral data that can proxy creditworthiness. For instance, sales volume trends (month-on-month revenue growth) indicate whether an SME’s business is expanding or contracting. Sharp declines or volatility can flag risk, while a steady upward trend suggests resilience. Likewise, refund and chargeback rates offer clues about product/service quality and fraud. A high return ratio or frequent disputed transactions typically lowers confidence. By contrast, a history of clean settlements boosts a merchant’s score. Customer retention and transaction frequency matter too: retailers with many repeat buyers and a high purchases-per-customer rate are less risky than those relying on one-off sales. Finally, seasonal revenue patterns can be evaluated: by analyzing year-over-year cycles, models can distinguish normal seasonal swings from abnormal shortfalls. For example, an electronics vendor might show predictable peaks around holidays or Ramadan; lenders can adjust expectations rather than misclassify the business as risky.

These signal types are already being used globally. Hong Kong’s PAObank, for example, “analyses real-time data from e‑commerce platforms, such as sales volume, inventory and refund rates” to “gain a comprehensive insight into SMEs’ business dynamics,” greatly streamlining credit assessment. In practice, a payment gateway may report total daily sales, number of transactions, average ticket value, plus refunds and disputes. Analytics engines apply machine learning to these metrics, learning which patterns (fast sales growth, low refunds, high repeat purchase rates, etc.) correlate with on-time repayment. Over time such models assign weights to each factor. For example, a loan scoring algorithm might up-weight consistent week-to-week revenue or low chargeback ratios, while down-weighting erratic, purely seasonal spikes. Importantly, data from multiple channels (in-store POS, e-commerce gateways, mobile wallets) can be combined for a 360° view of the business. In sum, payment and POS data provide a dynamic credit fingerprint that updates with every transaction.

Case Study: Embedded Underwriting Engines in MENA

New fintech platforms in the region are already embedding these signals into automated underwriting. For example, UAE-based fintechs like Fracxn and CredibleX partner with marketplaces and gateways to pull merchant transaction feeds into AI-driven credit engines. These platforms ingest daily sales data, invoicing records, supplier payments and similar flows. A real-time engine then computes a rolling risk score – adjusting it whenever fresh data arrives. Rapid sales growth will raise the score, while sudden slowdowns or spikes in refunds will lower it. Behind the scenes, machine-learning models learn from historical outcomes which patterns best predict defaults. While proprietary, these systems typically use calibrated weights: say, scoring a merchant’s gross merchandise volume (GMV) growth, refund ratio, repeat-customer rate, and debt service capacity together. Some engines even factor in cashflow timing (e.g. long invoice payment cycles may hurt scores). The net effect is that applications can be scored in minutes based on live data rather than weeks on static paperwork.

For instance, PAObank’s “Cross-border e-Commerce Revolving Loan” product in Hong Kong uses exactly this approach. Merchants submitting sales feeds can get a decision in about one week without any traditional financial statements – a speed made possible by algorithmic analysis of their transaction history. In MENA, similar embedded lending services allow SMEs to access receivables financing or revenue-based loans directly through their POS or marketplace apps. Every sale pushes the borrower’s underwriting forward in real time, so the risk profile is always current. As a result, SMEs with thin credit files can be scored alongside larger firms.

Benefits for Lenders

Incorporating gateway-derived data dramatically improves lenders’ risk differentiation and outreach. By augmenting sparse bureau files with rich transaction patterns, underwriters capture 5–20% more predictive power in their models. In practice this means fewer false negatives (creditworthy firms overlooked) and false positives (risky firms misclassified). Lenders report that AI-based scoring plus alternative data drives significantly higher approval rates at lower risk. For example, fintechs using these modern credit models have seen five-fold increases in approved applications from previously unbanked SMEs, while default and non-performing loans dropped by ~40%. Loan decisions also move much faster – Synapse Analytics notes as much as a 90% reduction in onboarding time when AI/alternative-data engines replace manual reviews.

Also read: AI and Alternative Data: Revolutionizing Credit Scoring for MENA's SMEs

In short, expanded data leads to both broader access and quicker decisions. Lenders can confidently extend small-ticket loans or lines-of-credit that would have been unaffordable under old models, knowing the risk score reflects actual merchant behavior. Middle East regulators and credit bureaus have also observed benefits: in the UAE, for example, the central credit bureau (AECB) has begun blending in non-traditional data to score applicants with no prior record. Ultimately, integrating payment data into underwriting not only unlocks new borrowers, it tends to improve overall portfolio performance. Studies show that lenders’ predictive accuracy improves by 5–20% when alternative signals are added to traditional data, translating into stronger credit portfolios.

Regulatory and Risk Considerations

As MENA lenders tap alternative data, emerging regulations and risks must be navigated. On one hand, many Gulf and North African governments encourage innovation: open-banking initiatives (launched in UAE, Bahrain, Saudi Arabia, Qatar, Egypt, etc.) are designed to unlock data (with consent) and foster fintech solutions. The UAE’s Al Etihad Credit Bureau even explicitly supports scoring with new data feeds. The Saudi and UAE central banks have piloted sandboxes for AI-based SME finance. Still, data privacy laws have tightened across MENA. Saudi Arabia’s Personal Data Protection Law (PDPL) – effective 2022 – codifies “credit data” and requires explicit user consent before processing it. Likewise, the UAE’s data protection regime (federal and in ADGM/DIFC zones) mandates transparency and lawful basis for all personal data use. In practice, lenders must ensure customers opt in before their transaction or POS data is used in scoring. They must also meet any security and retention requirements of local regulators.

Fairness is another concern. Advanced credit models can unintentionally encode bias – especially if alternative data proxies correlate with protected attributes. For example, policy researchers have warned that machine learning can widen disparities in outcomes (one US study found AI lending widened racial differences in interest rates). In MENA this calls for caution: algorithms must be audited to ensure they do not disadvantage groups (e.g. by relying on signals that systematically under-count women-owned businesses). Central banks and ESG-minded funds are increasingly asking fintech lenders to demonstrate fairness and explainability in their AI credit models. Regulators have not yet issued detailed rules specific to alternative data in MENA, but global best-practices apply: full disclosure to borrowers, robust data governance, and bias mitigation checks are essential. Finally, as fintechs integrate bank and gateway data, cybersecurity and data protection become critical. The OECD notes that expanded fintech usage increases risks of data breaches and fraud. MENA regulators therefore expect lenders to apply strong encryption, continuous monitoring, and clear privacy notices whenever new data sources are used.

Final Thoughts

The era of “beyond credit scores” is arriving in MENA: payment gateway and POS data are unlocking finance for SMEs that traditional models ignored. By mining trends like sales growth, refund ratios, customer repeat rates and seasonality, modern underwriters form a much richer risk picture. Sophisticated platforms (such as Fracxn in the UAE) now ingest these signals in real time, yielding dynamic risk scores and instant decisioning. The results speak for themselves: lenders report far higher approval rates, lower defaults, and dramatically shorter lending cycles. To seize this opportunity, banks and fintechs should weave gateway-derived metrics into their credit policies. Of course they must do so safely – complying with PDPL and open-banking rules, and vigilantly guarding against bias. But when done properly, leveraging transaction data from digital payments turns SMEs’ own business activity into creditworthy signals. In a region hungry for SME growth, that promise is powerful: integrating gateway data can instantly expand the pool of fundable businesses and help MENA’s entrepreneurs thrive.

Reference List

1. Al Etihad Credit Bureau (2024) AECB launches credit scoring for thin-file individuals and SMEs. Available at: https://aecb.gov.ae (Accessed: 4 July 2025).

2. International Finance Corporation (2021) MSME Finance Gap Report – MENA Region. Available at: https://www.smefinanceforum.org/data-sites/msme-finance-gap (Accessed: 4 July 2025).

3. Fracxn Technologies (2024) Embedded Lending and Real-Time Risk Scoring Engines. Available at: https://www.fracxn.com/ (Accessed: 4 July 2025).

4. Hong Kong Monetary Authority & PAObank (2023) Alternative Credit Assessment Methods in SME Lending. Available at: https://www.hkma.gov.hk/ (Accessed: 4 July 2025).

5. Mastercard Middle East (2023) SME Digital Readiness Index – MENA Edition. Available at: https://www.mastercard.com/news/mena (Accessed: 4 July 2025).

6. Saudi Data and Artificial Intelligence Authority (2022) Saudi PDPL: Personal Data Protection Law Overview. Available at: https://sdaia.gov.sa/ (Accessed: 4 July 2025).

7. Synapse Analytics (2023) AI Credit Scoring in MENA: Lender Productivity Case Studies. Available at: https://www.synapse-analytics.io/ (Accessed: 4 July 2025).

8. World Bank (2020) Financial Inclusion in MENA: Data and Analysis. Available at: https://www.worldbank.org/en/topic/financialinclusion (Accessed: 4 July 2025).

9. OECD (2022) AI and Responsible Lending in the Digital Age. Available at: https://www.oecd.org/finance/ai-responsible-lending.htm (Accessed: 4 July 2025).

10. Zawya (2023) MENA Fintech Lending Platforms and Open Banking Trends. Available at: https://www.zawya.com/en/markets/fintech/ (Accessed: 4 July 2025).