Abstract
Nowadays, the current application of artificial intelligence (AI) to financial context is opening a new field of study, named financial intelligence, in which the implementation of AI-based solutions as “financial brain” aims at assisting in complex decision-making (DM) processes as wealth and risk management, financial security, financial consulting, and blockchain. For venture capitalist organizations (VCOs), this aspect becomes even more critical, since different actors (shareholders, bondholders, management, suppliers, customers) with different DM behaviors are involved. One last layer of complexity is the potential variation of behaviors performed by managers even in presence of fixed organizational goals. The aim of this study is twofold: a general analysis of the debate on implementing AI in DM processes is introduced, and a proposal for modeling financial AI-based services is presented. A set of qualitative methods based on the application of cultural psychology is presented for modeling financial DM processes of all actors involved in the process, machines as well as individuals and organizations. The integration of some design thinking techniques with strategic organizational counseling supports the modeling of a hierarchy of selective criteria of fund-seekers and the creation of an innovative value proposition accordingly with goals of VCOs to be represented and supported in AI-based systems. Implications suggest that human/AI integration in the field can be implemented by developing systems where AI can be conceived in two distinct functions: (a) automation: treating Big Data from the market defined by management of VCO; and (b) support: creating alert systems that are coherent with ordered weighted decisional criteria of VCO.
Highlights
Artificial intelligence applied to decision-making (DM) processes has already been implemented in many fields (Galiano et al, 2019; Triberti et al, 2020; Bayrak et al, 2021) where it proves to have great potential
Model scouting criteria on fund-seekers to orient financial decisions. The result of this activity is twofold: on one side, it produced increased awareness in management on the complexity of DM processes they have to deal with and highlighted the need to share even implicit criteria used by different managers in different contextual circumstances; on the other, it produced descriptive charts of DM flows to be implemented in the platform
Once we observe it using descriptive tools from anthropology and psychology, we find more rationales that explain those behaviors in terms of personal and organizational contextualized objectives that actors are pursuing
Summary
Artificial intelligence applied to decision-making (DM) processes has already been implemented in many fields (Galiano et al, 2019; Triberti et al, 2020; Bayrak et al, 2021) where it proves to have great potential. Model scouting criteria on fund-seekers to orient financial decisions The result of this activity is twofold: on one side, it produced increased awareness in management on the complexity of DM processes they have to deal with and highlighted the need to share even implicit criteria used by different managers in different contextual circumstances; on the other, it produced descriptive charts of DM flows to be implemented in the platform. This process made it possible to align the system development team by highlighting two distinct roles of AI as potential support of FIGURE 1 | User journey map. This modeling method can foster in funding organizations the capacity of creating innovative services to be implemented in AI-based systems
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