Despite the significant role played by small and medium-sized enterprises in the Saudi Arabian economy, they face financial constraints that adversely affect their development and limit their potential to boost the economy to the desired target. SMEs create jobs, support innovation and boost the country’s exports. Even with the existence of 1.9 million SMEs in the country, they contribute little to Saudi Arabia’s GDP compared to advanced economies in the world. The enterprise-level survey by the World Bank indicated that SMEs experience limited access to credit from financial institutions compared to large corporations in developing countries like Saudi Arabia. Most SMEs lack qualified personnel with skills to provide financial information that has the same quality as those provided by large firms. Therefore, due to lack of audited financial statement, SMEs cannot access credit facilities.

In Saudi Arabia, financial institutions have shown growing interest in providing small and medium-sized businesses with credit; however, they are at the initial stages. Moreover, commercial banks, which are the primary source of funds for SMEs are still using traditional models when providing funds. The use of outdated model deprives SMEs of success due to the long time the model takes to process funds. SMEs are in need of loans to help them grow. Therefore, banks should develop a new data model that would help improve lending to SMEs. The new data model will reduce the time financial institutions take to process loans thus allowing SMEs to have easy access to loans. Conversely, the banking sector faces a major problem of loan defaulting risk assessment. Banks use predictive models to determine loan defaulters and those that are worth to access credit. Regarding SMEs, we will use data mining and work on a model to predict loan default.

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