Access to credit has long depended on traditional financial signals such as salary records, credit history, and established employment. But as the gig economy expands across the Middle East, these indicators increasingly fail to reflect how many people actually earn and manage money. Saudi fintech Tamara has addressed this gap by integrating verified financial data into its credit decisioning process, enabling the company to extend financing to a broader segment of modern workers.
Supporting the Modern Workforce
Tamara, founded in Riyadh, has quickly become one of Saudi Arabia’s leading fintech platforms, providing flexible payment solutions for millions of consumers and thousands of merchants across the Gulf Cooperation Council.
The company’s mission is clear. As Tamara describes it: “We help people own their dreams.” Its products are designed to give consumers simple and flexible ways to manage purchases and payments, allowing users greater control and confidence over their finances.
As the platform expanded regionally, Tamara identified a major shift in the workforce landscape. A growing number of people earn income through non-traditional paths including gig work, freelancing, part-time employment, and digital platforms.
While these users often maintain steady cash flow, they are frequently overlooked by traditional credit assessment models.
The Limitations of Traditional Credit Data
Conventional lending systems typically rely on historical financial data, including formal employment records, credit bureau histories, and fixed salary verification. These signals work well for salaried workers with stable employment but often fail to capture the real financial activity of individuals with variable or flexible income streams.
For fintech companies like Tamara, this creates a challenge. Potentially creditworthy users may be declined simply because traditional data sources do not reflect their financial reality.
Recognizing this gap, Tamara sought a way to gain deeper visibility into customers’ financial health in order to expand access responsibly.
Leveraging Verified Financial Data
To address the issue, Tamara partnered with financial data infrastructure provider Lean. The integration allowed Tamara to incorporate verified financial data directly into its credit decisioning framework.
By analysing real-time financial activity, Tamara was able to better understand applicants’ income flows and spending behaviour. This provided a more complete picture of affordability and financial stability, particularly for users with irregular or non-traditional income.
The result was a significant expansion in credit accessibility.
According to the case study, Tamara increased credit approvals by 32 percent, enabling more users from the modern workforce to access financing while maintaining responsible lending standards.
Building More Inclusive Financial Services
The partnership demonstrates how fintech platforms are using financial data infrastructure to redesign credit evaluation models. Rather than relying solely on static historical records, lenders can now analyse dynamic financial data that reflects how consumers actually manage money.
For Tamara, the approach supports its broader mission of making financial services more accessible across the region.
As digital economies expand across the Middle East, tools that provide deeper financial insight could play a critical role in enabling inclusive lending and supporting the next generation of consumers and entrepreneurs.
Readers can explore the full case study here: View Case Study
What this means for the industry
- Traditional credit scoring models are increasingly outdated for gig and freelance workers.
- Verified financial data can provide lenders with real-time visibility into income and affordability.
- Fintech platforms are using open banking infrastructure to expand responsible credit access.
- More inclusive lending models could unlock significant new customer segments across the GCC.
- Data-driven credit decisioning may become a standard approach for digital lenders globally.
