A multi-layer AI decision support system for startup success prediction and risk assessment using knowledge graphs and federated learning
ChengEn Pan, Xiaohong Pan, Li Sun · Scientific Reports · 2026
Since start-ups have grown so quickly in recent decades, it is more important than ever to determine what elements contribute to their success or failure. Numerous factors, such as market conditions, product differentiation, finance availability, and managerial methods, influence these results. However, precise forecasting is a constant issue due to the intricacy of business ecosystems and the interaction of non-financial and financial aspects. A multi-layer AI-driven prediction model that incorporates early-stage start-ups’ financial and non-financial characteristics is presented in this paper. A Graph Convolutional Network (GCN) creates feature embeddings at Layer 1, whereas a knowledge graph documents the connections between affecting factors. Federated learning is used to safely combine dispersed knowledge while maintaining privacy. Layer 2 uses a deep neural network (DNN) to forecast success or failure based on the fused features. Layer 3 offers risk assessment and interpretability, detecting survival variables including team dynamics and product differentiation. An experimental sample of 20 start-ups was thoroughly examined as part of the model’s evaluation on a dataset collected from Crunchbase that included approximately 623,000 companies, 799,000 founders, and 227,000 funding events. The findings show increased forecast accuracy and emphasize the value of non-financial elements in addition to conventional financial measurements. Through the integration of sophisticated AI approaches with organized domain knowledge, our work connects theoretical frameworks with empirical data. The suggested model contributes to start-up research and the real-world implementation of AI in business analytics by offering entrepreneurs, investors, and legislators a strong decision-support tool.