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Crowdfunding platforms promise to disrupt investing as they bypass traditional financial institutions through peer-to-peer transactions. To stay functional, these platforms require a supply of investors who are willing to contribute to campaigns. Yet, little is known about the retention of investors in this setting. Using four years of data from a leading equity crowdfunding platform, we empirically study the length and success of investor activity on the platform. We analyze temporal variations in these outcomes and explain patterns using statistical modeling. Our models are based on information about user's past and current investment decisions, i.e., content-based and structural similarities between the campaigns they invest in. We uncover the role of past successes and diversity of investment decisions for novice vs. serial investors. Our results inform potential strategies for increasing the retention of investors and improving their decisions on crowdfunding platforms. 

Crowd financing is a burgeoning phenomenon that promises to improve access to capital by enabling borrowers with limited financial opportunities to receive small contributions from individual lenders towards unsecured loan requests. Faced with information asymmetry about borrowers' credibility, individual lenders bear the entire loss in case of loan default. Predicting loan payment is therefore crucial for lenders and for the sustainability of these platforms. To this end, we examine whether the ''wisdom'' of the lending crowd can provide reliable decision support with respect to projects' long-term success. Using data from Prosper.com, we investigate the association between the dynamics of lending behaviour and successful loan payment through interpretable classification models. We find evidence for collective intelligence signals in lending behaviour and observe variability in crowd wisdom across loan categories. We find that the wisdom of the lending crowd is most prominent in the auto loan category, but it is statistically significant for all other categories except student debt. Our study contributes new insights on how signals deduced from lending behaviour can improve the efficiency of crowd financing thereby contributing to economic growth and societal development.