Study shows how big data and artificial intelligence can be leveraged to develop a more informative credit model to support the UAE SME ecosystem.
The ADGM Academy Research Centre in collaboration with the Asian Institute of Digital Finance (AIDF) at the National University of Singapore published the Leveraging Artificial Intelligence to Enhance the SME Ecosystem in the UAE white paper. The study shows how big data and artificial intelligence can be leveraged to develop a more informative credit model to support the UAE’s SME ecosystem and drive improved decision making when lending to SMEs.
SMEs represent the backbone of most of the world’s economies. As a result, they are getting thoughtful attention from planners, economists, governments, and multilateral agencies for their impact on national economies and the continuous market dynamics they are likely to contribute in creating job opportunities, enhancing productivity, fostering innovation, generating tax revenues and achieving the Sustainable Development Goals (SDGs) by delivering more inclusive economic growth, environmental sustainability, promoting sustainable industrialisation, reducing income inequalities, and alleviating poverty. Worldwide they represent more than 90% of all companies, generate between 60% and 70% of employment and are responsible for 50% of the Gross Domestic Product (GDP). In 2020 there were over 322 million formal SMEs, employing more than 705 million people. However, SMEs face challenges in securing financing to grow their business.
The study details the structural barriers SMEs encounter when trying to access financing, including challenges perceived on both the demand and supply sides of the ecosystem. A significant challenge is the Know Your Customer (KYC) process, which is time consuming and cumbersome for SMEs due to anti-money laundering and combatting the financing of terrorism requirements. Opening a bank account can take up to three months in some cases.
Generally, SMEs have less publicly available information compared to larger corporates. As a result, banks have more difficulty assessing the creditworthiness of SMEs, so credit decisions take longer, involve higher costs and require collateral. The study presents a case study on an innovative solution to reduce this information asymmetry between borrowers and lenders.
“The paper highlights the pain points felt by parties in the ecosystem pertaining to SME financing in the UAE and Abu Dhabi, and proposes a simple, yet robust, solution that paves the way to create an artificial intelligence and big data analytics-based Credit Bureau 3.0, a coordinated but decentralised digital platform,” says Jin-Chuan Duan, Executive Director of AIDF and Jardine Cycle & Carriage Professor of Finance, National University of Singapore.
Privacy-preserved data sharing among creditors can develop more reliable credit models through federated learning. The models can better measure borrowers’ creditworthiness at a lower cost and thus help reverse lenders’ low-risk appetite to engage with SMEs. Such data sharing, in effect, operates on the principles of coopetition, where lenders cooperate to create a shared infrastructure while competing. The proposed federated learning platform targets interpretable credit risk models that combine artificial intelligence, machine learning and big data aiming to reduce information asymmetries between debtors and creditors for all participating lending institutions but still allows them to build an individual competitive edge. The shared infrastructure lowers information acquisition costs to lenders and passes down the savings to borrowers via competition.
“The research demonstrates that a coopetition structure enables the creation of an improved credit model as a public good shared by all lenders, who can still compete based on risk appetite, services, and operational efficiency,” says Jassim Al Marzooqi, Associate Director, Business Enablement and Research, ADGM Academy. “SMEs, and ultimately the UAE economy, will benefit greatly from such an approach.”
“There is a great deal of literature that discusses the SME ecosystem in general or that focuses on the most important component of this model, which is access to finance. However, there are few works that go beyond a review of the well-known facts to provide practical solutions that are applicable and could have a significant impact on proper application,” says Mrs. Mouza Obaid Al Nasri, Executive Director of the SMEs Sector, Abu Dhabi Department of Economic Development. “This white paper is one of those distinguished studies that is able to diagnose the SMEs ecosystem accurately and comprehensively in the UAE and Abu Dhabi, as well as describe one of the successful experiences, which is to create a model that employs artificial intelligence to assist banks in making decisions regarding financing the SMEs that apply to them. This model will have a significant impact on the SMEs landscape in the UAE.”
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