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Terminology such as FinTech credit, embedded finance, alternative financing, embedded lending, buy now, pay later (BNPL), digital lending, non-bank lending, peer-to-peer (P2P) lending, invoice financing, supply chain financing, FinTech lending, online lending, and alternative credit describe the use of technology in providing financial services, specifically credit, outside of traditional banking. These terms encompass FinTech credit companies, which use data analysis and advanced analytics to assess creditworthiness and offer flexible and cost-effective financing options compared to conventional banks, revolutionizing the financial industry. Technology has made financial services available to everyone, expanded credit availability to underserved populations, and made the financial system more accessible and sustainable.
The FinTech sector has transformed owing to technological advancements, such as machine learning (ML), artificial intelligence (AI), blockchain, and digitalization. These innovations have revolutionized financial services and raised questions regarding their impact, effectiveness, and sustainability. Research from various disciplines has emerged to address these questions and provide insight into the evolving landscape of FinTech and its intersection with AI, blockchain, digitalization, and sustainable finance. The first study, by Nasir et al. (2021), used bibliometric analysis to identify key sources, research streams, and themes in the FinTech literature. It highlighted the potential of FinTech to improve financial operations while preserving the environment. Key research streams include cryptocurrencies, financial industry stability and innovation, and machine-learning innovations. A complementary study by Herrmann and Masawi (2022) identified the banking, financial services, and insurance (BFSI) sector as an early adopter of AI technologies. The study emphasized the significance of academic research in driving responsible AI adoption in BFSI and highlighted the dominant research areas of investments, risk management, and compliance. Hanafizadeh and Amin (2023) explored the services provided by FinTech companies in the banking sector. They identified 22 domains in which Fintech companies offer banking services, ranging from banking operations to risk and compliance. The study emphasized the importance of recognizing these services and underlined the collaborative potential between FinTech companies and traditional banks for digital transformation.
Calderon-Monge and Ribeiro-Soriano (2023) extensively reviewed the critical tendencies of digitalization in management, marketing, and finance. This study underscored the transformative potential of digitalization in driving economic growth and innovation. Broby (2022) conducted a comprehensive review of predictive analytics in finance, focusing on the application of various predictive analytics methods. The study highlighted the role of statistical and computational models in enhancing forecasting abilities and decision-making in finance. Vergallo and Mainetti (2022) focused on the role of technology in improving customer experience in banking. They identified key trends and themes in the literature and emphasized the potential of technology to personalize services and enhance convenience and customer support.
Despite the insightful research on FinTech’s evolution and the impact of various technologies, a critical gap remains in understanding the specific applications of ML and AI for credit scoring and risk management within FinTech lending. While existing studies have explored the broad applications of AI in the BFSI sector and predictive analytics in finance, a dedicated investigation into the specific ML and AI methods employed by FinTech companies for credit assessment and risk mitigation is lacking. This systematic literature review aims to address this gap by comprehensively analyzing the existing research on ML and AI methods utilized by FinTech companies for credit scoring and risk management, providing valuable insights into this rapidly evolving domain. In addition to contributing to the existing knowledge base on FinTech lending, this study also informs practitioners and policymakers in developing and implementing responsible and practical AI-powered credit assessment and risk management practices within the FinTech industry.