Strategic Financial Intelligence: Using Machine Learning to Inform Partnership Driven Growth in Global Payment Networks

Abstract

Throughout this extensive study, we present a thorough analysis of partnership-driven growth within payment networks. We provide an in-depth and detailed explanation of the diverse dataset, which comes from the global payment messages and includes a meticulous examination of the processing and matching pipeline that is necessary to effectively coalesce company and network data from multiple varying sources. We then benchmark the significant impact on network profitability by thoroughly evaluating transactional variables, firm fundamentals, and the intricate structure of the network. Using various attributes related to node importance, such as the number of unleashed partnership opportunities, internal and external centrality metrics, and community structure, we diligently develop customized indicators designed to measure how different types of corporate actions can enable firms to fill their liquidity needs within the network more efficiently. To suggest how financial intelligence and insights could also be translated into strategic action, thus preparing the grounds for accurately predicting whether firms would predominantly benefit from internal network adjustments versus external organizational reconfigurations, we subsequently turn our attention to advanced machine learning models. In conclusion, we summarize and discuss potential directions and promising avenues for future research in this vital area. This not only highlights the importance of evolving payment networks but also the opportunities that arise from strategic partnerships and innovative technological implementations that can drive growth and stability within financial ecosystems.