The Future of SME Lending: Innovations in Risk Assessment and Credit Scoring Models Using Machine Learning in Fintech

Abstract

The paper looks into how recent innovations like the use of advanced data analytics and machine learning influence SME funding. International socio-economic growth driven by SMEs has been a challenge to access funds since they lack credible credit histories and need to qualify for financial credit scores. These conventional approaches that tend to largely rely on written financial records negatively categorize them as high risk, thereby denying them much-needed funds. This paper discusses how these innovations, supervised and unsupervised learning, deep learning, and natural language processing, offer solutions to these problems using alt data and real-time data to manage risk effectively and sustainably. In addition, the examples of Kabbage and Funding Circle proved that such an approach is beneficial by decreasing the loan's default rate and the time required to consider the applications. Still, this paper examines issues like data privacy, adverse impacts of biased algorithms, and combining with longstanding banking systems; this paper also explores opportunities in machine learning and blockchain in SME lending in the future. Over time, through these technologies, customer loans access anew through efficiency, effectiveness, and transparency with a view of promoting SMEs' competitiveness in the new world market.