Credit bureaus in Egypt: A closer look at I-Score - Decisimo
In Egypt, the credit bureau market is led by a single entity known as I-Score, or The Egyptian Credit Bureau. I-Score holds a central place in the credit system. It maintains credit data for individuals and small and medium-sized enterprises, or SMEs.
Establishment and Legal Framework
I-Score was established on August 3, 2005, after approval from the Capital Market Authority and the Central Bank of Egypt. It was set up under Companies Law No. 159 of 1981 and Capital Market Law No. 95 of 1992.
Scope and Range of Data
I-Score maintains a credit information database that covers both SMEs and consumers. The database gives the lending community a clear signal. It supports credit approvals and improves trust in decision logic.
Members and Subscribers
Based on the latest available data, I-Score had 913 banks, companies, and microfinance institutions subscribed to its services. That subscriber base shows its role in the Egyptian credit market.
The Unbanked Population
However, a large share of Egypt's population remains unbanked. Reports suggest that more than 50 million individuals were unbanked out of an estimated total population of around 110 million in 2021. That represents at least 67% of the Egyptian adult population.
A main reason cited for being unbanked is insufficient income. Almost 45% of unbanked individuals say they do not have enough money to hold a bank account.
Impact on the Credit Market
By providing broad coverage of credit data for individuals and SMEs, I-Score has supported financial inclusion and economic growth in Egypt. It has also helped ensure that the lending community has access to reliable credit information. That matters for assessing creditworthiness, managing credit risk, and supporting responsible lending practices.
Implications and Challenges
The high share of the unbanked population creates challenges for credit bureaus like I-Score. A large segment of the population lacks formal financial records. That makes it hard for them to be part of the formal credit system, and they are not represented in I-Score's database.
Alternative Data for Financial Inclusion
One way to address this challenge is the use of alternative data. Credit bureaus and financial institutions can use non-traditional data sources such as mobile phone usage, utility bill payments, and other forms of informal financial activity. That can help assess the creditworthiness of individuals without a formal banking history. It can also support greater financial inclusion and give the unbanked population better access to credit and other financial services.
Frequently asked questions
How should I handle an Egyptian SME with no I-Score file?
Treat a missing I-Score file as unknown credit history, not evidence of poor repayment. Ask for records that show the business’s cash generation, such as customer-provided bank statements, invoices, or utility-payment history where the business can lawfully provide it. Refer cases with gaps for review, and size any offer to verified repayment capacity.
Which alternative data should I test first for Egyptian SMEs?
Start with business cash-flow records that are direct, verifiable, and tied to the borrower, such as bank transactions and customer invoices. Then test utility-payment history if it is available and relevant to the business. Mobile phone data may describe an individual rather than the enterprise, so check that it adds useful evidence and does not become a weak proxy for repayment capacity.
How can I test whether alternative data improves SME lending decisions?
Compare decisions with and without the new data on a time-based holdout, using repayment outcomes over a defined period that matches your loan terms. Check default rates, calibration, approval rates, and results across borrower groups. A model built only on previously approved borrowers can miss applicants who were declined, so treat its apparent performance with caution.
How should I set an I-Score cutoff for Egyptian SME loans?
Do not use a universal I-Score cutoff. Set approval and referral bands from your risk appetite and loan economics, including expected loss, calculated from probability of default, loss given default, and exposure at default. Use I-Score as one input, and send thin-file or borderline applications for review rather than treating a missing score as a failing score.
