Scoring Approaches to Assessing the Investment Attractiveness of Innovative Projects

This work is dedicated to the development of a scoring model for evaluating the investment potential of innovative financial technology (fintech) projects based on a variety of qualitative criteria, including technological, market, organizational, human, and regulatory factors. The first chapter examines the key trends and challenges facing the fintech industry and their impact on traditional banking. It also reviews international experience with using scoring models for evaluating fintech projects and identifies their limitations. This chapter justifies the importance of considering qualitative factors when assessing startup investment potential. In the second chapter, a set of qualitative criteria for evaluating fintech projects was proposed based on a review of scientific literature. Hypotheses about their impact on success were formulated, and a prototype of a predictive model was developed using the gradient boosting algorithm (XGBoost). The empirical testing of this model on real data confirmed its high generalizing ability, with an precision of 0.71 and an ROC AUC of 0.85. The third chapter focuses on the development of practical recommendations for banks regarding the implementation of this model. The strategic importance of advanced analytics in the digital transformation of the financial sector was substantiated. Key stakeholder groups were identified, and approaches to their involvement were proposed. A roadmap for integrating this model into the IT infrastructure and business processes of banks was also developed.The results of this study have both theoretical and practical significance. The theoretical significance lies in the conceptualization and operationalization of the qualitative factors that determine the investment attractiveness of fintech projects. The practical significance lies in the development and testing of applied tools for investment decision-making by banks.