Abstract:
In the modern era, fintechs are recognized as critical players within the financial industry, with new lending technologies rapidly evolving. These technologies have the potential to transform traditional lending processes by enhancing accessibility, speed, and efficiency. This study aims to identify and rank new lending technologies employed by fintechs. The research methodology is qualitative, with a statistical population comprising 12 senior employees from the research and planning department of Bank Tejarat. Initially, based on the existing literature and prior research, emerging lending technologies, along with relevant criteria and sub-criteria, were identified and finalized through expert validation. Using the fuzzy DEMATEL method, the interrelationships among organizational, technical, operational, and financial-economic criteria were analyzed. Results indicated that the technical criterion had the highest influence, while the organizational criterion was the most influenced. Subsequently, fuzzy ANP was utilized to assign weights to the criteria and sub-criteria. Finally, rankings were conducted using two fuzzy methods, SAW (Simple Additive Weighting) and TOPSIS, entirely within Excel. By averaging the results of both SAW and TOPSIS methods, the final ranking of 17 sub-criteria was obtained, with artificial intelligence, big data, blockchain, payment systems, and cloud computing securing the top five positions, respectively. Artificial intelligence emerged as the top choice in this analysis. This research can assist financial institutions and fintech startups in selecting and implementing the appropriate technology to effectively enhance lending performance.