Predicting Startup Funding Momentum with Collective Intelligence

In this paper, we describe a set of techniques that leverage human and machine intelligence to create data and models that are both predictively accurate and support an in-depth explanation facility. Our approach leverages the cognitive diversity of an evaluation team with domain experience and investment experience. Each team, unique to each startup, participates in an interactive evaluation process where each team member uses available evidence to score the potential for a startup to produce a return on investment. All startups are then tracked for performance following scoring and prediction. To date, the approach outlined here has demonstrated high predictive accuracy.Key to the explanatory power of our approach is collecting natural language data on reasons behind a particular score. All supporting reasons for all scores are collected and sampled to learn the rank and relevance determined by group responses. Each reason is assigned a relevancy score based on structured interactions with the evaluating team. A typical evaluation will consist of ~200 quantitative data items and ~10,000 words.Thematic analysis of the supporting reasons for scores is produced by training an NLP model to map reasons into a predetermined set of themes typically used in evaluating investments. Feedback from the evaluating teams and startup founding teams allow for continuous training of explanation system.We show that an archi- tecture of interoperable models are highly effective in achieving both accuracy and explanatory power in investment evaluations.