Mathematical Model and Method of Decision Making in Economic Systems Based on Deep Learning

Recently, peer-to-peer lending through e-commerce platforms has been on the rise. However, investors naturally need to assess investment risks effectively. In this paper, we propose a novel approach for risk assessment in P2P investments, integrating the interval-stochastic analytic hierarchy process (AHP) with artificial intelligence (AI). While traditional deterministic AHP selects the best alternative based on a single global priority value, our interval-stochastic AHP model introduces interval-based global priorities, increasing decision complexity but enhancing robustness. The proposed methodology is demonstrated with a case study, and comparisons with interval arithmetic-based methods highlight their limitations. Furthermore, we implement a neural network with two hidden layers to predict the expected return on investment (ROI), enhancing the decision-making process. This research provides the first integration of the interval-stochastic AHP method with AI -driven ranking for investment decisions under heterogeneous interval uncertainty.