Algorithms for systematizing due diligence processes for early-stage innovative projects

This article formalizes and describes algorithms for systematizing due diligence procedures in selecting early-stage, innovative projects for venture capital and private equity. This paper is relevant given the growing deal flow, the increasingly complex information landscape surrounding startups, and investors' increasing sensitivity to the quality of due diligence. The paper's scientific novelty lies in the development of an integrated algorithmic framework combining a selection funnel, a multi-level evidence structure, scoring scales, and rules for terminating due diligence upon reaching confidence thresholds. The paper describes the stages of pre-investment analysis, examines scoring procedures and methods for minimizing information loss, and considers the risks associated with "quick" investing in overheated segments. Particular attention is paid to the standardization of due diligence artifacts: a hypothesis matrix, a risk register, and a map of signals for the team, market, product, and exit. The goal of the study is to propose a reproducible due diligence algorithm for early stages. To achieve this goal, an analytical synthesis of publications and a comparative interpretation of selection models were used. The conclusion presents results on the process structure and decision quality management. This material is helpful for investors, managers, instructors of original courses, and analysts.