<p> <em>By Roshan Shah</em></p>
<p>Small and medium enterprises (SMEs) play a pivotal role in contributing to the global economy, with increasing opportunities for employment and innovation. However, access to credit remains a significant challenge for most of them. Even in India, owing to the trade finance gap, small businesses often face challenges due to a lack of sufficient credit. There are nearly 63.4 million MSMEs in the country, and only 15-20% have access to formal credit.</p>
<p>The tool of credit scoring encompasses a set of variable factors that help evaluate the creditworthiness of a business. Traditionally, it was based on collateral, the applicant’s past behavior, credit history, including credit card spending, income statements, loan trails, etc., and extensive documentation. However, the traditional credit scoring models often fall short in assessing the creditworthiness of these businesses. They tend to exclude businesses that are underbanked or do not have a credit history.</p>
<p><strong>The surging popularity of alternative data for credit scoring and risk assessment</strong></p>
<p>With rapid digitization and advent of innovative solutions in the fintech space, alternative data and methods have gained popularity for credit scoring and risk assessment. The alternative credit scoring method utilises data from alternative sources such as e-commerce websites, social media platforms, digital transactions, etc., helping lenders make informed decisions for loan approvals.</p>
<p>The global alternative data market is projected to exhibit a 50.6% CAGR growth between 2024 and 2030. The surging demand for alternative data sources combined with the usage of AI-based analytical tools such as ML (Machine Learning) and NLP (Natural Language Processing) is expected to drive the industry’s growth. In India, the increasing usage of alternative data by financial service providers will lead to the sector’s development.</p>
<p>Leveraging Artificial Intelligence (AI) with alternative credit data and embedded scoring models enables accurate and predictive credit scoring. The integration of alternative data and AI in recent years has emerged as a promising solution, offering both benefits and challenges in SME credit scoring and risk assessment.</p>
<h3><strong>Benefits of using AI and alternative data</strong></h3>
<p>Let’s first look at the benefits of using AI and alternative data:</p>
<p><strong>Improved fraud detection</strong></p>
<p>By analysing non-traditional data sources such as social media, online transactions, and business behavior patterns, AI models gain a comprehensive understanding of an SME’s creditworthiness. This approach minimizes reliance on traditional credit metrics, allowing for a nuanced assessment that captures a business’ true financial health. Improved fraud detection is achieved through real-time analysis, swiftly identifying anomalies and potential risks. The synergy of alternative data and AI not only refines SME credit scoring but also bolsters the overall resilience of financial systems against fraudulent activities.</p>
<p><strong>Risk management</strong></p>
<p>Many SMEs lack a robust financial history or collateral, making it difficult for them to secure credit through traditional means. Alternative data sources, such as utility payments, online sales data, and social media behavior, can help assess the creditworthiness of businesses.</p>
<p><strong>Cost reduction</strong></p>
<p>AI-powered credit scoring systems can process vast amounts of data at high speeds, enabling quicker decision-making. This is crucial for SMEs that often require prompt access to funds for business operations, expansion, or to capitalise on emerging opportunities.</p>
<p><strong>Challenges associated with leveraging alternative data and AI</strong></p>
<p>While there are various benefits of using AI-based tools for assessing the credit scores for small businesses, the process comes with its fair share of challenges. Let’s have a glimpse at some of the loopholes:</p>
<p><strong>Privacy and security concerns</strong></p>
<p>Integrating alternative data sources raises concerns about privacy and security. SMEs must navigate the ethical use of data, ensuring compliance with regulations and protecting sensitive information. This challenge necessitates the development of robust data protection measures and transparency in the handling of information.</p>
<p><strong>Potential risk of inaccurate assessment</strong></p>
<p>Unlike established enterprises, many SMEs have limited historical data, making it challenging to build accurate credit scoring models. AI algorithms may struggle to provide reliable predictions with insufficient historical information, leading to potential inaccuracies in risk assessments.</p>
<p><strong>Lack of transparency</strong></p>
<p>AI models, particularly complex deep learning algorithms, often lack transparency. The lack of interpretability can be a significant hurdle in gaining the trust of financial institutions, regulators, and SMEs. In such a scenario, clear and understandable explanations of how decisions are reached are crucial for wider acceptance and adoption.</p>
<p><strong>To conclude</strong></p>
<p>Leveraging alternative data and AI for SME credit scoring and risk assessment holds immense potential for transforming the lending landscape of the fintech industry. The benefits include improved accuracy, inclusion of underrepresented businesses, faster decision-making, and adaptability. However, addressing challenges related to data privacy, limited historical data, interpretability, and bias is crucial for the responsible and ethical implementation of these technologies. As the financial industry continues to evolve, finding a balance between innovation and accountability will be essential in harnessing the full potential of alternative data and AI for SME credit assessment in the coming times.</p>
<p><em>The author is the co-founder and CEO at VoloFin.</em></p>
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