Challenges SMEs in emerging markets face and how we might overcome them
Can machine learning help solve a fraction of these problems?
According to The World Bank, Small and medium-sized enterprises (SMEs) represent about 90% of businesses and more than 50% of employment worldwide. Formal SMEs contribute up to 40% of emerging economies’ national income (GDP). This makes SME development a high priority for many governments around the world. In emerging markets, most formal jobs are generated by SMEs, which create 7 out of 10 jobs.
Growing up in a semi-urban area, I was fortunate to witness the incredible impact SMEs have on my community. They have created a dynamic and vibrant ecosystem in my neighborhood. bringing the streets alive with bustling activity adorned with colorful shops, small manufacturing units, and innovative startups. It was within these bustling streets that I discovered my own inspiration, as I witnessed the unwavering determination of entrepreneurs who fearlessly navigated the challenges and seized the opportunities that came their way.
Driven by my passion for designing for impact, I embarked on various projects targeting SMEs in Nigeria and Kenya. These countries are renowned for their thriving business sectors, characterized by their remarkable ability to innovate and swiftly adapt to ever-changing market conditions. Unlike larger corporations weighed down by bureaucracy, SMEs have the freedom to experiment, test new ideas, and swiftly bring novel solutions to the market. Their innovations have not only driven technological advancements but also improved productivity and fostered a sense of overall competitiveness.
How did we do it
Background
Part of the world bank's initiative is to help SMEs Improve access to Finance and Economic Opportunities in Africa. The aim is to promote financial inclusion by linking members of informal savings groups with formal financial institutions to promote sound resource management and resilience to shocks, particularly for women-owned or led SMEs.
The Problem/challenge
SMEs have limited access to finance, inadequate infrastructure, regulatory constraints, skill gaps, and market uncertainties. By ensuring the survival and continuity of SMEs, during times of economic crises, financial aid contributes to overall economic stability and resilience. During our research, we quickly discovered the majority of these SMEs don’t have access to credit hence hindering their business expansion and potential. We crafted a problem statement that could help us answer this challenge and use the limited time we had to see it through
“ How might we address the significant constraints faced by SMEs in emerging markets when accessing financial institutions for funding and credit?”
Discovery: Understanding the problem
In our recent field research in Nigeria and Kenya, we discovered significant challenges faced by SMEs in accessing financial institutions for funding and credit
- SMEs, especially newly established businesses, have limited or no credit history.
- Lack of proper financial documentation or incomplete records makes it difficult for financial institutions to evaluate their financial health and repayment capacity.
- These SMEs have limited assets or struggle to meet the collateral requirements set by the financial institutions. In such cases, they face difficulties in accessing loans or credit.
- Financial institutions heavily rely on credit history to assess repayment capacity and determine interest rates. Without a solid credit history, SMEs may face challenges in obtaining loans or credit.
- Incomplete financial statements, unorganized bookkeeping, or inconsistent financial reporting can create doubts about the SME’s financial stability and hinder loan approvals.
- Financial institutions often require collateral as security for loans.
The design challenge:
SMEs in emerging markets face difficulties obtaining loans from banks due to a lack of credit history. Banks determine interest rates based on credit scores, which assess an individual’s risk by analyzing their past credit activity using statistical models.
“ How might we use AI and machine learning to create a credit scoring system for SMEs in emerging markets that is more efficient and accurate than traditional methods, and that addresses the challenge of limited credit history?”
Ideation
Imagine a world where your creditworthiness is evaluated not by humans alone, but by advanced algorithms and cutting-edge technology. This is the realm of AI-powered credit scoring, a game-changer in the financial industry. While I can’t reveal all the details of the ongoing development work, I can provide you with a glimpse into the fascinating process of building a credit score using AI.
The journey begins with meticulous research and development. Experts are tirelessly working to refine and enhance these AI models, ensuring they can handle diverse scenarios and capture the nuances of credit evaluation. While much of the development work remains under wraps, allow me to unveil a curated general approach to building a credit score using AI.
Prototyping
Picture a sophisticated system that collects essential data points, ranging from your personal information to your financial records, loan applications, and repayment patterns. These valuable insights are then fed into the AI algorithms, which become the virtuoso conductors orchestrating the credit scoring process.
At its core, AI credit scoring harnesses the potential of machine learning algorithms and data analytics. These powerful tools delve deep into vast amounts of data, carefully examining your financial history, spending patterns, and more. By analyzing this wealth of information, AI algorithms can determine your creditworthiness with remarkable accuracy.
But AI credit scoring is not just about crunching numbers; it’s about extracting meaningful patterns and connections. The algorithms scrutinize your financial history, searching for red flags, commendable patterns, and unique indicators of creditworthiness. This intricate dance between data and algorithms paints a comprehensive portrait of your financial health.
Testing and iterations
Rigorous testing and validation are conducted to ensure the reliability and accuracy of the AI credit scoring model. This involves simulating various scenarios, stress-testing the algorithms, and fine-tuning them for optimal performance. The goal is to create a robust system that can handle real-world challenges and deliver dependable credit assessments.
While the specific details of this ongoing project may remain a mystery for now, rest assured that brilliant minds are tirelessly working to revolutionize credit scoring. AI-powered credit assessment has the potential to transform the lending landscape, granting fair and efficient access to financial opportunities.
So, as you navigate the realm of credit and financing, remember the incredible advancements underway. AI, with its ability to unlock hidden insights and revolutionize credit scoring, promises a future where financial decisions are made with unprecedented precision and fairness.
Conclusion
It is important to note that building a credit score using AI requires a robust data infrastructure, data privacy, and security measures, and adherence to legal and ethical guidelines surrounding the use of personal and financial data. Collaboration with credit bureaus, financial institutions, and regulatory bodies is essential to establish standardized practices and ensure the responsible use of AI in credit scoring.
Here are some articles, that I found interesting and worth taking into consideration while undertaking this type of project
